From d7361f8880ab7a3047a4e7775273d4293083a5d9 Mon Sep 17 00:00:00 2001
From: RomirJ <playindus@gmail.com>
Date: Mon, 12 Oct 2026 11:59:24 -0700
Subject: [PATCH 1/8] Move LeRobot pin to 0.6.1 with draccus 0.11.6 (Studio ADR
 0003)

draccus 0.11.6 drops the GPL-3.0+ pyyaml-include that draccus 0.10.0 (required
by lerobot 0.5.1) pulled in. transformers stays 5.3.0 by override, so the
monolithic exporter's masking-API gate is unchanged.

- One QUALIFIED_LEROBOT_VERSION constant (src/tether/_lerobot_version.py) for
  the monolithic exporter gate, local dataset admission and preflight text.
- Local dataset admission pins the LeRobot 0.6.1 wheel, source commit
  7e241bd and 13 reviewed upstream file hashes (diffs reviewed in Studio
  docs/research/verification/lerobot-06-implementation-20261011/), plus the
  Studio v2 statistics adapter hash.
- lerobot.types -> lerobot.lerobot_types; GR00T probe imports follow N1.7.
- Resume refuses LeRobot 0.5.x train configs with a typed
  cross-version-resume-unsupported code.
- pi0/pi0.5/GR00T parity vehicles pin the 0.6.1 runtime, write to a new
  volume subtree and take --tether-revision/--watchdog-seconds.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 pyproject.toml                                | 48 ++++++++++++++-----
 scripts/modal_gr00t_lerobot_probe.py          | 10 ++--
 scripts/modal_groot_export_parity_pinned.py   | 25 ++++++----
 scripts/modal_pi05_export_parity_pinned.py    | 26 ++++++----
 scripts/modal_pi0_export_parity_pinned.py     | 17 ++++---
 src/tether/_lerobot_version.py                |  3 ++
 src/tether/cli.py                             |  2 +-
 .../distill/pi05_state_out_processor.py       |  2 +-
 src/tether/eval/preflight.py                  |  2 +-
 src/tether/exporters/decomposed.py            |  2 +-
 src/tether/exporters/monolithic.py            | 10 ++--
 src/tether/finetune/local_dataset.py          | 40 ++++++++--------
 src/tether/finetune/preflight/schema.py       |  6 ++-
 src/tether/finetune/resume.py                 | 16 ++++++-
 tests/integration/test_eval_aloha_smoke.py    |  2 +-
 tests/test_cli_export_end_to_end.py           |  7 +--
 tests/test_finetune_local_dataset.py          |  8 ++--
 tests/test_finetune_resume_command.py         |  9 ++++
 tests/test_lora_argv_parser.py                |  8 ++--
 tests/test_one_command_deploy.py              |  2 +-
 20 files changed, 159 insertions(+), 86 deletions(-)
 create mode 100644 src/tether/_lerobot_version.py

diff --git a/pyproject.toml b/pyproject.toml
index cab571f8..5235dc98 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,7 +20,8 @@ classifiers = [
 ]
 
 dependencies = [
-    "torch>=2.1.0",
+    "torch==2.7.1; platform_machine != 'aarch64'",
+    "torch>=2.1.0; platform_machine == 'aarch64'",
     "safetensors>=0.4.0",
     "typer>=0.9.0",
     "rich>=13.0.0",
@@ -29,11 +30,11 @@ dependencies = [
     # compiled against numpy 1.x ABI; numpy 2.x's ABI break corrupts ORT +
     # JAX wheels on aarch64. On x86_64 (Modal A100, customer desktops) +
     # arm64 (M-series Mac dev hosts) numpy 2.x is fine, AND the
-    # [monolithic] extra's lerobot==0.5.1 dep REQUIRES numpy>=2.0 — pinning
+    # [monolithic] extra's lerobot==0.6.1 dep REQUIRES numpy>=2.0 — pinning
     # numpy<2 globally makes lerobot uninstallable on Modal (caught by the
     # 2026-05-20 lift #4 smoke fire). Split via PEP-508 platform marker.
     "numpy>=1.24.0,<2.0; platform_machine == 'aarch64'",
-    "numpy>=1.24.0; platform_machine != 'aarch64'",
+    "numpy==2.2.6; platform_machine != 'aarch64'",
     "pyyaml>=6.0",
     # Always needed: hub access for `tether export <hf_id>` and tokenizer/config
     # loading for the auto-detect path. Apr-14 install-path verification caught
@@ -141,13 +142,17 @@ comply = []
 # gate in GOALS.yaml. Heavy (pulls torch, torchvision, accelerate, etc.) —
 # that's why it's a separate extra, not a base dep.
 #
-# lerobot 0.5.x requires Python >=3.12. Jetson (JetPack 6) ships 3.10,
+# lerobot 0.6.x requires Python >=3.12. Jetson (JetPack 6) ships 3.10,
 # so we gate lerobot behind a version marker — pip skips it on 3.10/3.11
 # instead of hard-failing with ResolutionImpossible. Native parity
 # verification should be run on a Python 3.12+ host.
 native = [
-    "lerobot==0.5.1; python_version >= '3.12'",
+    "transformers==5.3.0",
+    "lerobot==0.6.1; python_version >= '3.12'",
+    "draccus==0.11.6; python_version >= '3.12'",
     "num2words",
+    "accelerate>=1.10,<2",
+    "diffusers==0.39.0",
 ]
 # Monolithic ONNX export — cos=1.0 verified production path. Runs locally
 # via `tether export --monolithic` (the DEFAULT as of v0.2.1). Requires
@@ -155,18 +160,21 @@ native = [
 # masking_utils.sdpa_mask that breaks onnx-diagnostic patches). Base pin
 # `transformers>=4.40,<5.4` permits this extra to pin-to-exact.
 #
-# lerobot 0.5.x requires Python >=3.12. On Jetson (Python 3.10) the
+# lerobot 0.6.x requires Python >=3.12. On Jetson (Python 3.10) the
 # monolithic export can't run locally — export on a cloud/desktop host
 # (Python 3.12+) and `tether serve` the resulting ONNX on the Jetson
 # with `pip install 'fastcrest-tether[serve]'` only.
 monolithic = [
-    "lerobot==0.5.1; python_version >= '3.12'",
+    "lerobot==0.6.1; python_version >= '3.12'",
+    "draccus==0.11.6; python_version >= '3.12'",
     "transformers==5.3.0",
     "onnx-diagnostic>=0.9",
     "onnxscript>=0.1",
     "optree",
     "scipy",
     "num2words",
+    "accelerate>=1.10,<2",
+    "diffusers==0.39.0",
 ]
 # Task-success evaluation (heavy sim deps). Gated behind this extra so the
 # base install stays light. Use with `tether bench --benchmark libero_10`.
@@ -177,8 +185,15 @@ eval = [
     "gymnasium",
     "h5py",
 ]
+# Spell out the SmolVLA dependencies: its upstream transformers-dep extra
+# requires >=5.4, while ADR 0003 keeps the qualified 5.3.0 masking API.
+# This equivalent explicit set also resolves with pip, which ignores tool.uv.
 eval-local = [
-    "lerobot[smolvla]==0.5.1; python_version >= '3.12' and sys_platform == 'linux'",
+    "transformers==5.3.0; python_version >= '3.12' and sys_platform == 'linux'",
+    "num2words>=0.5.14,<0.6; python_version >= '3.12' and sys_platform == 'linux'",
+    "accelerate>=1.10,<2; python_version >= '3.12' and sys_platform == 'linux'",
+    "lerobot==0.6.1; python_version >= '3.12' and sys_platform == 'linux'",
+    "draccus==0.11.6; python_version >= '3.12' and sys_platform == 'linux'",
     "peft; python_version >= '3.12' and sys_platform == 'linux'",
     "mujoco==3.3.2; sys_platform == 'linux'",
     "robosuite==1.4.1; sys_platform == 'linux'",
@@ -212,13 +227,14 @@ tracing = [
 #   pip install 'fastcrest-tether[lerobot]'
 #   tether serve bundle/ --embodiment so_arm100 --port /dev/ttyUSB0
 #
-# lerobot 0.5.x requires Python >=3.12 (we skip on 3.10/3.11 to avoid pip's
+# lerobot 0.6.x requires Python >=3.12 (we skip on 3.10/3.11 to avoid pip's
 # ResolutionImpossible; users on JetPack 6 Python 3.10 can still construct an
 # adapter + run the conversion math, they just can't open a serial bus until
 # they upgrade to Python 3.12 or install scservo_sdk directly via the [so100]
 # extra).
 lerobot = [
-    "lerobot>=0.5.1; python_version >= '3.12'",
+    "lerobot==0.6.1; python_version >= '3.12'",
+    "draccus==0.11.6; python_version >= '3.12'",
 ]
 # SO-100 hardware extra — installs the scservo_sdk Python package on the
 # Raspberry Pi / host wired to the arm. Separate from [lerobot] because the
@@ -234,9 +250,10 @@ so100 = [
 # RTC-enabled deployments. Skeleton + config validation work without this
 # extra; only the actual processor construction needs it.
 #
-# lerobot 0.5.x requires Python >=3.12 (skipped on 3.10/3.11).
+# lerobot 0.6.x requires Python >=3.12 (skipped on 3.10/3.11).
 rtc = [
-    "lerobot==0.5.1; python_version >= '3.12'",
+    "lerobot==0.6.1; python_version >= '3.12'",
+    "draccus==0.11.6; python_version >= '3.12'",
 ]
 # Curate format-converter extras. Phase 1 ships LeRobot v3 + HDF5 with the
 # core install (parquet via pyarrow which huggingface_hub already pulls; h5py
@@ -270,7 +287,7 @@ curate-rlds = [
 
 # Jetson / ARM64 edge stack.
 #   - Skips [gpu] x86_64 wheels (onnxruntime-gpu PyPI has no aarch64 builds)
-#   - Skips lerobot (0.5.1 requires Python 3.12; JetPack 6 ships 3.10)
+#   - Skips lerobot (0.6.1 requires Python 3.12; JetPack 6 ships 3.10)
 #   - User must pre-install Jetson Zoo onnxruntime-gpu from pypi.jetson-ai-lab.io
 #     and mount host CUDA via --runtime=nvidia.
 jetson = [
@@ -370,3 +387,8 @@ markers = [
     "hardware: requires a physical robot wired in; skipped unless RUN_HARDWARE_TESTS=1",
     "receipt: validates a signed or provenance-bound operational receipt",
 ]
+
+[tool.uv]
+# ADR 0003 owner decision: retain the qualified exporter masking API despite
+# LeRobot 0.6.1 transformers-dep declaring >=5.4,<5.6. Install extras with uv.
+override-dependencies = ["transformers==5.3.0"]
diff --git a/scripts/modal_gr00t_lerobot_probe.py b/scripts/modal_gr00t_lerobot_probe.py
index 15b75f47..34402a28 100644
--- a/scripts/modal_gr00t_lerobot_probe.py
+++ b/scripts/modal_gr00t_lerobot_probe.py
@@ -1,6 +1,6 @@
-"""Quick probe — what does lerobot 0.5.1 actually expose for GR00T?
+"""Quick probe — what does lerobot 0.6.1 actually expose for GR00T?
 
-lerobot-0.5.1 on Modal may or may not have GR00T support. The cloned
+lerobot-0.6.1 on Modal may or may not have GR00T support. The cloned
 local lerobot is `main` (newer). Need to reconcile.
 
 Prints the actual module contents of lerobot's groot subpackage.
@@ -23,7 +23,7 @@
         "numpy",
         "safetensors>=0.4.0",
         "huggingface_hub",
-        "lerobot==0.5.1",
+        "lerobot==0.6.1",
         "num2words",
     )
 )
@@ -52,7 +52,7 @@ def probe():
 
     # Attempt to import groot_n1 + list symbols
     try:
-        from lerobot.policies.groot import groot_n1
+        from lerobot.policies.groot import groot_n1_7 as groot_n1
         print(f"\ngroot_n1 symbols (top-level, non-dunder):")
         for name in sorted(dir(groot_n1)):
             if not name.startswith("_"):
@@ -64,7 +64,7 @@ def probe():
 
     # Attempt action_head
     try:
-        from lerobot.policies.groot.action_head import flow_matching_action_head as fma
+        from lerobot.policies.groot.action_head import cross_attention_dit as fma
         print(f"\nflow_matching_action_head symbols:")
         for name in sorted(dir(fma)):
             if not name.startswith("_"):
diff --git a/scripts/modal_groot_export_parity_pinned.py b/scripts/modal_groot_export_parity_pinned.py
index 05d30434..380efe41 100644
--- a/scripts/modal_groot_export_parity_pinned.py
+++ b/scripts/modal_groot_export_parity_pinned.py
@@ -46,7 +46,7 @@
 )
 
 HF_CACHE_PATH = "/root/.cache/huggingface"
-PARITY_OUT_PATH = "/parity_out"
+PARITY_OUT_PATH = "/parity_out/lerobot-061-20261011"
 
 
 def _hf_secret():
@@ -63,7 +63,8 @@ def _hf_secret():
     modal.Image.debian_slim(python_version="3.12")
     .apt_install("git")
     .pip_install(
-        "torch",
+        "torch==2.7.1",
+        "torchvision==0.22.1",
         "safetensors>=0.4.0",
         "huggingface_hub",
         # transformers==5.3.0 EXACTLY: the monolithic exporter refuses
@@ -73,14 +74,15 @@ def _hf_secret():
         "onnxruntime>=1.20",
         "onnxscript>=0.1",
         # monolithic dep group (export_gr00t_monolithic gates on these):
-        "lerobot==0.5.1",
+        "lerobot==0.6.1",
         "num2words",
+        "diffusers==0.39.0",
         "onnx-diagnostic>=0.9",
         "optree",
         "scipy",
         "accelerate",
-        "draccus",
-        "numpy",
+        "draccus==0.11.6",
+        "numpy==2.2.6",
         "Pillow",
         "typer",
         "rich",
@@ -122,7 +124,7 @@ def _hf_secret():
     timeout=3600,
     volumes={
         HF_CACHE_PATH: _hf_cache_volume,
-        PARITY_OUT_PATH: _parity_out_volume,
+        "/parity_out": _parity_out_volume,
     },
     secrets=[_hf_secret()],
 )
@@ -130,7 +132,7 @@ def run_export_parity(
     model_revision: str = GROOT_REVISION,
     tether_revision: str = TETHER_REVISION,
     embodiment_id: int = 0,
-    reuse_export: bool = True,
+    reuse_export: bool = False,
     watchdog_seconds: float = 3300,
 ) -> dict:
     import subprocess
@@ -322,8 +324,13 @@ def _progress(stage: str) -> None:
 
 
 @app.local_entrypoint()
-def main() -> None:
-    result = run_export_parity.remote()
+def main(
+    tether_revision: str = TETHER_REVISION,
+    watchdog_seconds: float = 1800,
+) -> None:
+    result = run_export_parity.remote(
+        tether_revision=tether_revision, watchdog_seconds=watchdog_seconds
+    )
     print("\n=== RESULT ===")
     for key, value in result.items():
         print(f"  {key}: {value}")
diff --git a/scripts/modal_pi05_export_parity_pinned.py b/scripts/modal_pi05_export_parity_pinned.py
index c54ed686..c5c68c99 100644
--- a/scripts/modal_pi05_export_parity_pinned.py
+++ b/scripts/modal_pi05_export_parity_pinned.py
@@ -47,7 +47,7 @@
 )
 
 HF_CACHE_PATH = "/root/.cache/huggingface"
-PARITY_OUT_PATH = "/parity_out"
+PARITY_OUT_PATH = "/parity_out/lerobot-061-20261011"
 
 
 def _hf_secret():
@@ -61,9 +61,12 @@ def _hf_secret():
     modal.Image.debian_slim(python_version="3.12")
     .apt_install("git")
     .pip_install(
-        # Install lerobot first — it pins torch>=2.7 and hub>=1.0
-        "lerobot==0.5.1",
+        # Qualify the same torch/vision runtime as the CPU probes.
+        "torch==2.7.1",
+        "torchvision==0.22.1",
+        "lerobot==0.6.1",
         "num2words",
+        "diffusers==0.39.0",
         "safetensors>=0.4.0",
         "onnx>=1.16",
         "onnxruntime>=1.20",
@@ -71,9 +74,9 @@ def _hf_secret():
         "onnx-diagnostic>=0.9",
         "optree",  # onnx-diagnostic soft-dep
         "scipy",  # onnx-diagnostic patches_transformers_qwen2_5 transitively
-        "numpy",
+        "numpy==2.2.6",
         "accelerate",
-        "draccus",
+        "draccus==0.11.6",
     )
     # transformers==5.3.0 EXACTLY: the monolithic exporter refuses anything
     # else (5.4+ has a q_length regression in masking_utils.sdpa_mask).
@@ -119,7 +122,7 @@ def _hf_secret():
     timeout=3600,
     volumes={
         HF_CACHE_PATH: _hf_cache_volume,
-        PARITY_OUT_PATH: _parity_out_volume,
+        "/parity_out": _parity_out_volume,
     },
     secrets=[_hf_secret()],
 )
@@ -127,7 +130,7 @@ def run_export_parity(
     model_revision: str = PI05_BASE_REVISION,
     tether_revision: str = TETHER_REVISION,
     num_steps: int = 10,
-    reuse_export: bool = True,
+    reuse_export: bool = False,
     watchdog_seconds: float = 3300,
 ) -> dict:
     import subprocess
@@ -324,8 +327,13 @@ def _progress(stage: str) -> None:
 
 
 @app.local_entrypoint()
-def main() -> None:
-    result = run_export_parity.remote()
+def main(
+    tether_revision: str = TETHER_REVISION,
+    watchdog_seconds: float = 1800,
+) -> None:
+    result = run_export_parity.remote(
+        tether_revision=tether_revision, watchdog_seconds=watchdog_seconds
+    )
     print("\n=== RESULT ===")
     for key, value in result.items():
         print(f"  {key}: {value}")
diff --git a/scripts/modal_pi0_export_parity_pinned.py b/scripts/modal_pi0_export_parity_pinned.py
index 52d5e10b..18c39430 100644
--- a/scripts/modal_pi0_export_parity_pinned.py
+++ b/scripts/modal_pi0_export_parity_pinned.py
@@ -39,7 +39,7 @@
 )
 
 HF_CACHE_PATH = "/root/.cache/huggingface"
-PARITY_OUT_PATH = "/parity_out"
+PARITY_OUT_PATH = "/parity_out/lerobot-061-20261011"
 
 
 def _hf_secret():
@@ -53,9 +53,12 @@ def _hf_secret():
     modal.Image.debian_slim(python_version="3.12")
     .apt_install("git")
     .pip_install(
-        # Install lerobot first — it pins torch>=2.7 and hub>=1.0
-        "lerobot==0.5.1",
+        # Qualify the same torch/vision runtime as the CPU probes.
+        "torch==2.7.1",
+        "torchvision==0.22.1",
+        "lerobot==0.6.1",
         "num2words",
+        "diffusers==0.39.0",
         "safetensors>=0.4.0",
         "onnx>=1.16",
         "onnxruntime>=1.20",
@@ -63,9 +66,9 @@ def _hf_secret():
         "onnx-diagnostic>=0.9",
         "optree",  # onnx-diagnostic soft-dep
         "scipy",  # onnx-diagnostic patches_transformers_qwen2_5 transitively
-        "numpy",
+        "numpy==2.2.6",
         "accelerate",
-        "draccus",
+        "draccus==0.11.6",
     )
     # transformers==5.3.0 EXACTLY: the monolithic exporter refuses anything
     # else (5.4+ has a q_length regression in masking_utils.sdpa_mask).
@@ -111,7 +114,7 @@ def _hf_secret():
     timeout=3600,
     volumes={
         HF_CACHE_PATH: _hf_cache_volume,
-        PARITY_OUT_PATH: _parity_out_volume,
+        "/parity_out": _parity_out_volume,
     },
     secrets=[_hf_secret()],
 )
@@ -320,11 +323,13 @@ def main(
     tether_revision: str = TETHER_REVISION,
     reuse_export: bool = False,
     num_steps: int = 10,
+    watchdog_seconds: float = 1800,
 ) -> None:
     result = run_export_parity.remote(
         tether_revision=tether_revision,
         reuse_export=reuse_export,
         num_steps=num_steps,
+        watchdog_seconds=watchdog_seconds,
     )
     print("\n=== RESULT ===")
     for key, value in result.items():
diff --git a/src/tether/_lerobot_version.py b/src/tether/_lerobot_version.py
new file mode 100644
index 00000000..a88eefb8
--- /dev/null
+++ b/src/tether/_lerobot_version.py
@@ -0,0 +1,3 @@
+"""LeRobot runtime qualified for dataset admission and export (ADR 0003)."""
+
+QUALIFIED_LEROBOT_VERSION = "0.6.1"
diff --git a/src/tether/cli.py b/src/tether/cli.py
index 4ccbf222..ac787baf 100644
--- a/src/tether/cli.py
+++ b/src/tether/cli.py
@@ -2370,7 +2370,7 @@ def serve(
              "lerobot's RTCProcessor so the robot keeps executing the tail "
              "of one chunk while the next chunk is being computed. 2-3× "
              "effective throughput on Jetson-class latency. Requires "
-             "`pip install fastcrest-tether[rtc]` (pulls lerobot==0.5.1).",
+             "`pip install fastcrest-tether[rtc]` (pulls lerobot==0.6.1).",
     ),
     rtc_execution_horizon: int = typer.Option(
         10,
diff --git a/src/tether/distill/pi05_state_out_processor.py b/src/tether/distill/pi05_state_out_processor.py
index 6e0e5ef4..466be057 100644
--- a/src/tether/distill/pi05_state_out_processor.py
+++ b/src/tether/distill/pi05_state_out_processor.py
@@ -32,7 +32,7 @@ def _register_step() -> type:
     import (matching the pattern in snapflow_pi0_model.py).
     """
     from lerobot.processor.pipeline import ProcessorStep, ProcessorStepRegistry
-    from lerobot.types import EnvTransition, TransitionKey
+    from lerobot.lerobot_types import EnvTransition, TransitionKey
 
     @ProcessorStepRegistry.register(name="pi05_prepare_tokenizer_state_out")
     @dataclass
diff --git a/src/tether/eval/preflight.py b/src/tether/eval/preflight.py
index 324386ad..3b09eaa1 100644
--- a/src/tether/eval/preflight.py
+++ b/src/tether/eval/preflight.py
@@ -172,7 +172,7 @@ def __post_init__(self) -> None:
     ),
     "dep-version-conflict": (
         "Dependency version mismatch detected. Pin: robosuite==1.4.1, "
-        "bddl==1.0.1, mujoco==3.3.2, lerobot==0.5.1. Use the bundled "
+        "bddl==1.0.1, mujoco==3.3.2, lerobot==0.6.1. Use the bundled "
         "Modal image (--runtime modal) for known-good pins."
     ),
     "osmesa-compile-hang": (
diff --git a/src/tether/exporters/decomposed.py b/src/tether/exporters/decomposed.py
index 204eb64e..bd3e747d 100644
--- a/src/tether/exporters/decomposed.py
+++ b/src/tether/exporters/decomposed.py
@@ -38,7 +38,7 @@
 
 ## Dependencies
 
-Same as ``monolithic`` extra: transformers==5.3.0, lerobot==0.5.1,
+Same as ``monolithic`` extra: transformers==5.3.0, lerobot==0.6.1,
 onnx-diagnostic>=0.9. ``apply_export_patches`` from ``monolithic.py``
 is invoked here to get the shared denoise-step + cache-freeze patches.
 
diff --git a/src/tether/exporters/monolithic.py b/src/tether/exporters/monolithic.py
index 59c64a55..4a58ed39 100644
--- a/src/tether/exporters/monolithic.py
+++ b/src/tether/exporters/monolithic.py
@@ -10,7 +10,7 @@
     tether export lerobot/smolvla_base --monolithic --output ./smol
 
 Requires (pinned in the ``monolithic`` extra):
-    lerobot==0.5.1
+    lerobot==0.6.1
     transformers==5.3.0   (5.4+ has a q_length scalar regression)
     onnx-diagnostic>=0.9
     onnxscript>=0.1
@@ -23,6 +23,8 @@
 """
 from __future__ import annotations
 
+from tether._lerobot_version import QUALIFIED_LEROBOT_VERSION
+
 import logging
 import time
 from importlib.metadata import PackageNotFoundError, version as _dist_version
@@ -175,14 +177,14 @@ def _require_monolithic_deps() -> None:
                 "lerobot imports but package metadata is missing; install with: "
                 "pip install 'fastcrest-tether[monolithic]'"
             ) from exc
-        if lerobot_version != "0.5.1":
+        if lerobot_version != QUALIFIED_LEROBOT_VERSION:
             raise ImportError(
                 f"lerobot {lerobot_version} detected; monolithic export requires "
-                "exactly lerobot==0.5.1. Install with: "
+                f"exactly lerobot=={QUALIFIED_LEROBOT_VERSION}. Install with: "
                 "pip install 'fastcrest-tether[monolithic]'"
             )
     except ImportError as e:
-        missing.append(f"lerobot==0.5.1 ({e})")
+        missing.append(f"lerobot=={QUALIFIED_LEROBOT_VERSION} ({e})")
 
     for mod_name, pip_name in [
         ("onnx_diagnostic", "onnx-diagnostic>=0.9"),
diff --git a/src/tether/finetune/local_dataset.py b/src/tether/finetune/local_dataset.py
index 1cb452fd..f1921819 100644
--- a/src/tether/finetune/local_dataset.py
+++ b/src/tether/finetune/local_dataset.py
@@ -8,6 +8,8 @@
 
 from __future__ import annotations
 
+from tether._lerobot_version import QUALIFIED_LEROBOT_VERSION
+
 import hashlib
 import json
 import math
@@ -18,25 +20,25 @@
 from typing import Any, NoReturn
 
 
-ADAPTER_SOURCE = "11c0a0cc0d5f4cd81de9904ec0d08f0305ba9ecec6e5952c25fe435c857afdf3"
-WRITER_SOURCE = "1396b9fab7aecddd10006c33c47a487ffdcb54b4"
-WRITER_WHEEL = "bbd11021023fde0947b6d1ff1c52fe91c86a28ab09a96359892f3ef7e8866862"
+ADAPTER_SOURCE = "af6969a6663d0b288db781ad46736eb62d33a361dbe75cd578da6c163b075307"
+WRITER_SOURCE = "7e241bd630a3719a56157a497ce5d08f244784f1"
+WRITER_WHEEL = "1894516040c65f80a45bd9741f8174aae90ed5d93da0627ab4f1a85fd8d75e90"
 WRITER_FILES = {
-    "lerobot/datasets/lerobot_dataset.py": "03207172bbc82d541f93168017b11e2b3807cf719126bf47d04873257a4cbbd8",
-    "lerobot/datasets/utils.py": "1dd05df5f1d12721f227fb3601d99c22cd845cb59627b68004d3184c9e8e1486",
-    "lerobot/datasets/compute_stats.py": "7b0621bbdd36987939742a1803b3491b794db7b0e6393677e9b1a3e1d86aeafc",
-    "lerobot/datasets/dataset_writer.py": "f377ab064840d45f2dab98da27f5c71b5dfb64f70789fec5c82935eecc925207",
-    "lerobot/datasets/dataset_reader.py": "c5893ecb6406a6852f01dcc4d6662f6ca2320cd931bc9e4d1cd7dc83dfaa9d6c",
-    "lerobot/datasets/dataset_metadata.py": "df392d4c0b584177735550251c45877ba7b644377003e3fa08b71536c9969b59",
-    "lerobot/datasets/io_utils.py": "1b557bab9f1f1445602dfd5dc5bca69a3fe2c34c400ae36d3c69b5c7d4d1120a",
-    "lerobot/datasets/feature_utils.py": "1477145c9f27051511e661f5189f838d8fdfacdb0c7c8424a64d4c181027ce7a",
-    "lerobot/datasets/image_writer.py": "99ce7371c50b7fb0931b49228fa5e3ae30302e5559efd7f64dead6334aa9e088",
+    "lerobot/datasets/lerobot_dataset.py": "07523e2a4f9c53b6a96df9255ad131ee30e707fd8610ec9d2178d3741023ae3a",
+    "lerobot/datasets/utils.py": "1fd3eddf70baf1c5c7f4e745c78338c580385283fad00e853d49cd0f34dd57e3",
+    "lerobot/datasets/compute_stats.py": "d546a5c690eff716a10e9b13d70cd5d1ef7fed88f2f9c40c99f2f7f50845cf45",
+    "lerobot/datasets/dataset_writer.py": "319f4efb53e892ceeb439aae6d25209d14564e3dc91134c7cc2c6c0b5e71ebc5",
+    "lerobot/datasets/dataset_reader.py": "3a477dae6aa887449f09be25b385485b065861d13c5448b9369a0ba09f01bb5d",
+    "lerobot/datasets/dataset_metadata.py": "23c927553a53e320161f35f9b36e8b61a446833ca17a4287b9ef5cdc8d19c50f",
+    "lerobot/datasets/io_utils.py": "af08c7875f34f8469f80c4fd903af12971d6fa96c179be984887b93ef2f6f91c",
+    "lerobot/datasets/feature_utils.py": "928369cff50f0d663a125abfc67683ccbae0e552ed2eb22edde9e6bcff2aef81",
+    "lerobot/datasets/image_writer.py": "54cad218c99caad105ea8738507ee87fc8c2858832d31aa5b21d324c97360631",
 }
 NORMALIZATION_FILES = {
-    "lerobot/processor/normalize_processor.py": "faedf40f64e1fcc6aeb287ff757abe6e85983560d82fcde060252d8f75e277d6",
-    "lerobot/policies/act/configuration_act.py": "11aa9037f981a957d63632561c795fac243e7898ee9302f7afac2f5d45920f7b",
-    "lerobot/policies/smolvla/configuration_smolvla.py": "6c1f371ccb7ed073774c459387f85626edcf568edbbaa64b4cea1bf70a80feca",
-    "lerobot/configs/default.py": "e077db856b4ecad9476bdf12fb2e72a0294ae3a302413afa21774a8788ab0d20",
+    "lerobot/processor/normalize_processor.py": "f0cd88bec4c954a93ba1cc90300473b96eb177762789c1780cf07801fc1af9d6",
+    "lerobot/policies/act/configuration_act.py": "35a60c0dd3c811b137b5eb035bdbd40d40e2c24ba0cda61756052e6e1c829282",
+    "lerobot/policies/smolvla/configuration_smolvla.py": "2fb637cb428fa2fdf1d114646dcffaf4728216bfe5b7039d5d0cac4857ffc4e0",
+    "lerobot/configs/default.py": "e8307a48aa97c1b3fd0722e9a849deb1bc1bb48b63e3109b63683eb7ffe08105",
 }
 IDENTITY_FIELDS = ["task", "seed", "episode", "initial_state", "embodiment"]
 PARTITIONS = ("development", "holdout", "test")
@@ -605,13 +607,13 @@ def _verify(dataset_root: Path, expected_manifest_sha256: str, expected_profile:
     inventory = _inventory(export, receipt)
     writer = _object(receipt.get("writer"), "writer")
     _require(
-        writer.get("version") == "0.5.1"
+        writer.get("version") == QUALIFIED_LEROBOT_VERSION
         and writer.get("source_commit") == WRITER_SOURCE
         and writer.get("wheel_sha256") == WRITER_WHEEL
         and writer.get("source_files") == WRITER_FILES
-        and _object(writer.get("runtime_versions"), "writer runtime").get("lerobot") == "0.5.1",
+        and _object(writer.get("runtime_versions"), "writer runtime").get("lerobot") == QUALIFIED_LEROBOT_VERSION,
         "writer-pin",
-        "Export writer/source pins must match qualified LeRobot 0.5.1.",
+        f"Export writer/source pins must match qualified LeRobot {QUALIFIED_LEROBOT_VERSION}.",
     )
     info = _object(_json(root / "meta/info.json"), "dataset info")
     stats = _object(_json(root / "meta/stats.json"), "dataset statistics")
diff --git a/src/tether/finetune/preflight/schema.py b/src/tether/finetune/preflight/schema.py
index a2431018..cff8e462 100644
--- a/src/tether/finetune/preflight/schema.py
+++ b/src/tether/finetune/preflight/schema.py
@@ -13,6 +13,8 @@
 """
 from __future__ import annotations
 
+from tether._lerobot_version import QUALIFIED_LEROBOT_VERSION
+
 import logging
 from pathlib import Path
 from typing import Any
@@ -138,7 +140,7 @@ def check_schema(cfg: FinetuneConfig) -> PreflightCheck:
         expected_modes = {"VISUAL": "IDENTITY", "STATE": "MEAN_STD", "ACTION": "MEAN_STD"}
         if not isinstance(base_config, dict) or base_config.get("type") != "smolvla":
             return PreflightCheck("schema", "fail", "Local admission requires a resolvable SmolVLA base config.")
-        # An omitted field uses only the exact LeRobot 0.5.1 class defaults
+        # An omitted field uses only the exact LeRobot 0.6.1 class defaults
         # qualified in the verified receipt. Explicit null/contradictory modes
         # are not treated as omission.
         resolved_modes = base_config.get("normalization_mapping", expected_modes)
@@ -148,7 +150,7 @@ def check_schema(cfg: FinetuneConfig) -> PreflightCheck:
             "resolved_normalization_mapping": resolved_modes,
             "normalization_mapping_source": (
                 "base config.json" if "normalization_mapping" in base_config
-                else "pinned LeRobot 0.5.1 SmolVLAConfig defaults"
+                else f"pinned LeRobot {QUALIFIED_LEROBOT_VERSION} SmolVLAConfig defaults"
             ),
         }
         max_state = base_config.get("max_state_dim")
diff --git a/src/tether/finetune/resume.py b/src/tether/finetune/resume.py
index 19c4326a..92731b91 100644
--- a/src/tether/finetune/resume.py
+++ b/src/tether/finetune/resume.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import json
 import logging
 from pathlib import Path
 import subprocess
@@ -8,7 +9,9 @@
 
 
 class ResumeConfigurationError(ValueError):
-    pass
+    def __init__(self, message: str, *, code: str = "resume-configuration-invalid"):
+        self.code = code
+        super().__init__(message)
 
 
 def resolve_resume_checkpoint(output_dir: Path) -> tuple[Path, Path]:
@@ -39,11 +42,20 @@ def resolve_resume_checkpoint(output_dir: Path) -> tuple[Path, Path]:
         raise ResumeConfigurationError(
             f"resume checkpoint is missing pretrained_model/train_config.json: {checkpoint}"
         )
+    saved = json.loads(config.read_text())
+    if isinstance(saved, dict) and any(
+        key in saved for key in ("eval_freq", "use_rabc", "rabc_progress_path", "rabc_kappa", "rabc_epsilon", "rabc_head_mode")
+    ):
+        raise ResumeConfigurationError(
+            "cross-version-resume-unsupported: LeRobot 0.5.x training configs "
+            "cannot resume in the qualified 0.6.1 runtime; start a new run.",
+            code="cross-version-resume-unsupported",
+        )
     return checkpoint, config
 
 
 def build_resume_command(output_dir: Path) -> tuple[list[str], Path]:
-    """Build the LeRobot v0.5.1 resume command from the checkpoint's own config.
+    """Build the LeRobot v0.6.1 resume command from the checkpoint's own config.
 
     Do not repeat `--policy.path`, dataset, optimizer, scheduler, seed or other
     training settings. LeRobot's resume contract loads those values from the
diff --git a/tests/integration/test_eval_aloha_smoke.py b/tests/integration/test_eval_aloha_smoke.py
index 569de695..dcc1c39a 100644
--- a/tests/integration/test_eval_aloha_smoke.py
+++ b/tests/integration/test_eval_aloha_smoke.py
@@ -44,6 +44,6 @@ def test_aloha_two_episode_customer_flow(tmp_path):
     assert all(type(row["success"]) is bool for row in report["episodes"])
     assert report["confidence_interval"]["method"] == "wilson-score"
     assert receipt["policy"]["revision"] == REVISION
-    assert receipt["lerobot_version"] == "0.5.1" and receipt["device"] == "cpu"
+    assert receipt["lerobot_version"] == "0.6.1" and receipt["device"] == "cpu"
     assert receipt["migrated"] is True
     assert (output / "eval_info.json").is_file()
diff --git a/tests/test_cli_export_end_to_end.py b/tests/test_cli_export_end_to_end.py
index ee0b34a6..f7a7466b 100644
--- a/tests/test_cli_export_end_to_end.py
+++ b/tests/test_cli_export_end_to_end.py
@@ -69,7 +69,8 @@ def test_dep_check_catches_wrong_transformers(monkeypatch):
         monolithic._require_monolithic_deps()
 
 
-def test_dep_check_catches_wrong_lerobot(monkeypatch):
+@pytest.mark.parametrize("lerobot_version", ["0.4.4", "0.5.1"])
+def test_dep_check_catches_wrong_lerobot(monkeypatch, lerobot_version):
     """_require_monolithic_deps() must fail early on the known-bad
     lerobot 0.4.x stack instead of letting torch.export fail downstream."""
     from tether.exporters import monolithic
@@ -81,12 +82,12 @@ def test_dep_check_catches_wrong_lerobot(monkeypatch):
 
     def _fake_dist_version(dist_name: str) -> str:
         if dist_name == "lerobot":
-            return "0.4.4"
+            return lerobot_version
         raise monolithic.PackageNotFoundError(dist_name)
 
     monkeypatch.setattr(monolithic, "_dist_version", _fake_dist_version)
 
-    with pytest.raises(ImportError, match=r"lerobot 0\.4\.4.*lerobot==0\.5\.1"):
+    with pytest.raises(ImportError, match=r"lerobot " + lerobot_version.replace(".", r"\.") + r".*lerobot==0\.6\.1"):
         monolithic._require_monolithic_deps()
 
 
diff --git a/tests/test_finetune_local_dataset.py b/tests/test_finetune_local_dataset.py
index 5afbb96e..8015dc41 100644
--- a/tests/test_finetune_local_dataset.py
+++ b/tests/test_finetune_local_dataset.py
@@ -169,7 +169,7 @@ def file_hash(path):
     ]
     adapter = {
         "policy": "verified-moments-v1",
-        "adapter_source_sha256": "11c0a0cc0d5f4cd81de9904ec0d08f0305ba9ecec6e5952c25fe435c857afdf3",
+        "adapter_source_sha256": "af6969a6663d0b288db781ad46736eb62d33a361dbe75cd578da6c163b075307",
         "statistics_sha256": canonical(stats),
         "source_dataset_sha256": identity["sha256"],
         "split_sha256": split["sha256"],
@@ -220,11 +220,11 @@ def file_hash(path):
             "fps": 10,
         },
         "writer": {
-            "version": "0.5.1",
+            "version": "0.6.1",
             "source_commit": WRITER_SOURCE,
             "wheel_sha256": WRITER_WHEEL,
             "source_files": dict(WRITER_FILES),
-            "runtime_versions": {"lerobot": "0.5.1"},
+            "runtime_versions": {"lerobot": "0.6.1"},
         },
         "statistics_policy": "verified-moments-v1",
         "statistics_adapter": adapter,
@@ -717,7 +717,7 @@ def test_omitted_base_modes_use_only_pinned_defaults(local_cfg):
     assert result.severity == "ok"
     assert (
         result.detail["normalization_mapping_source"]
-        == "pinned LeRobot 0.5.1 SmolVLAConfig defaults"
+        == "pinned LeRobot 0.6.1 SmolVLAConfig defaults"
     )
     config["normalization_mapping"] = None
     write_json(path, config)
diff --git a/tests/test_finetune_resume_command.py b/tests/test_finetune_resume_command.py
index a7de3d3a..e6d9ea4b 100644
--- a/tests/test_finetune_resume_command.py
+++ b/tests/test_finetune_resume_command.py
@@ -47,3 +47,12 @@ def test_resume_refuses_checkpoint_missing_saved_train_config(tmp_path):
     checkpoint(tmp_path, 100, config=False)
     with pytest.raises(ResumeConfigurationError, match="train_config"):
         build_resume_command(tmp_path)
+
+
+def test_resume_refuses_legacy_lerobot_config(tmp_path):
+    model = tmp_path / "checkpoints" / "000003" / "pretrained_model"
+    model.mkdir(parents=True)
+    (model / "train_config.json").write_text('{"eval_freq": 100}')
+    with pytest.raises(ResumeConfigurationError, match="cross-version-resume-unsupported") as error:
+        build_resume_command(tmp_path)
+    assert error.value.code == "cross-version-resume-unsupported"
diff --git a/tests/test_lora_argv_parser.py b/tests/test_lora_argv_parser.py
index 44847f2e..5e60b68c 100644
--- a/tests/test_lora_argv_parser.py
+++ b/tests/test_lora_argv_parser.py
@@ -1,7 +1,7 @@
-"""Parse the real LoRA argv with LeRobot 0.5.1, offline and without weights.
+"""Parse the real LoRA argv with LeRobot 0.6.1, offline and without weights.
 
 Mirrors Studio's backend/tests/test_lora_argv_parser.py at the Tether boundary.
-Requires LeRobot 0.5.1 and PEFT. Inline resume only parses its flag; saved-config
+Requires LeRobot 0.6.1 and PEFT. Inline resume only parses its flag; saved-config
 resume restores the checkpoint path. Neither case runs training or qualifies
 dataset admission or interrupted-training recovery.
 """
@@ -29,7 +29,7 @@ def no_network(*args, **kwargs):
     monkeypatch.setattr(socket.socket, "connect_ex", no_network)
     pytest.importorskip("lerobot")
     pytest.importorskip("peft")
-    assert version("lerobot") == "0.5.1"
+    assert version("lerobot") == "0.6.1"
 
     import draccus
     from lerobot.configs import parser, policies
@@ -41,7 +41,7 @@ def no_network(*args, **kwargs):
     assert Path(run.__file__).resolve().is_relative_to(source)
     # Keep the real config loader and policy decoding; stub only hub lookup.
     policy_file = tmp_path / "config.json"
-    policy_file.write_text(json.dumps(draccus.encode(SmolVLAConfig(device="cpu"))))
+    policy_file.write_text(json.dumps({**draccus.encode(SmolVLAConfig(device="cpu")), "type": "smolvla"}))
 
     def hub_config(*args, **kwargs):
         assert kwargs["repo_id"] == "lerobot/smolvla_base"
diff --git a/tests/test_one_command_deploy.py b/tests/test_one_command_deploy.py
index 444cc5b3..e5dbbad7 100644
--- a/tests/test_one_command_deploy.py
+++ b/tests/test_one_command_deploy.py
@@ -338,7 +338,7 @@ def test_export_dep_missing_errors_with_monolithic_install_hint(self, runner, cl
 
         with patch(
             "tether.exporters.monolithic.export_monolithic",
-            side_effect=ImportError("Missing dependencies: lerobot==0.5.1, onnx-diagnostic"),
+            side_effect=ImportError("Missing dependencies: lerobot==0.6.1, onnx-diagnostic"),
         ):
             result = runner.invoke(cli_app, [
                 "go",

From e32c3ea8e4424aca994a6bae8c39f912936fc656 Mon Sep 17 00:00:00 2001
From: RomirJ <playindus@gmail.com>
Date: Mon, 12 Oct 2026 12:04:50 -0700
Subject: [PATCH 2/8] Review fixes for the LeRobot 0.6.1 pin

- Keep the base torch/numpy requirements as on main (torch>=2.1.0,
  numpy>=1.24.0 off aarch64). The exact torch==2.7.1 / numpy==2.2.6 base pins
  were outside ADR 0003 (which pins Studio's evaluation runtime, not every
  public install) and LeRobot 0.6.1 already declares torch<2.12, numpy<2.3.
- accelerate floor >=1.14 in the explicit SmolVLA dependency sets, matching
  lerobot 0.6.1's accelerate-dep extra (>=1.14,<2) instead of a looser 1.10.
- Resume: an unreadable train_config.json raises ResumeConfigurationError
  instead of a bare JSONDecodeError; test added.
- New test: WRITER_FILES/NORMALIZATION_FILES equal the installed bytes when
  the qualified LeRobot is present (a wrong pin previously passed every
  Tether test; mutation-checked).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 pyproject.toml                        | 11 +++++------
 src/tether/finetune/resume.py         |  5 ++++-
 tests/test_finetune_local_dataset.py  | 18 ++++++++++++++++++
 tests/test_finetune_resume_command.py |  8 ++++++++
 4 files changed, 35 insertions(+), 7 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 5235dc98..3c3d1f99 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,8 +20,7 @@ classifiers = [
 ]
 
 dependencies = [
-    "torch==2.7.1; platform_machine != 'aarch64'",
-    "torch>=2.1.0; platform_machine == 'aarch64'",
+    "torch>=2.1.0",
     "safetensors>=0.4.0",
     "typer>=0.9.0",
     "rich>=13.0.0",
@@ -34,7 +33,7 @@ dependencies = [
     # numpy<2 globally makes lerobot uninstallable on Modal (caught by the
     # 2026-05-20 lift #4 smoke fire). Split via PEP-508 platform marker.
     "numpy>=1.24.0,<2.0; platform_machine == 'aarch64'",
-    "numpy==2.2.6; platform_machine != 'aarch64'",
+    "numpy>=1.24.0; platform_machine != 'aarch64'",
     "pyyaml>=6.0",
     # Always needed: hub access for `tether export <hf_id>` and tokenizer/config
     # loading for the auto-detect path. Apr-14 install-path verification caught
@@ -151,7 +150,7 @@ native = [
     "lerobot==0.6.1; python_version >= '3.12'",
     "draccus==0.11.6; python_version >= '3.12'",
     "num2words",
-    "accelerate>=1.10,<2",
+    "accelerate>=1.14,<2",
     "diffusers==0.39.0",
 ]
 # Monolithic ONNX export — cos=1.0 verified production path. Runs locally
@@ -173,7 +172,7 @@ monolithic = [
     "optree",
     "scipy",
     "num2words",
-    "accelerate>=1.10,<2",
+    "accelerate>=1.14,<2",
     "diffusers==0.39.0",
 ]
 # Task-success evaluation (heavy sim deps). Gated behind this extra so the
@@ -191,7 +190,7 @@ eval = [
 eval-local = [
     "transformers==5.3.0; python_version >= '3.12' and sys_platform == 'linux'",
     "num2words>=0.5.14,<0.6; python_version >= '3.12' and sys_platform == 'linux'",
-    "accelerate>=1.10,<2; python_version >= '3.12' and sys_platform == 'linux'",
+    "accelerate>=1.14,<2; python_version >= '3.12' and sys_platform == 'linux'",
     "lerobot==0.6.1; python_version >= '3.12' and sys_platform == 'linux'",
     "draccus==0.11.6; python_version >= '3.12' and sys_platform == 'linux'",
     "peft; python_version >= '3.12' and sys_platform == 'linux'",
diff --git a/src/tether/finetune/resume.py b/src/tether/finetune/resume.py
index 92731b91..0fe57165 100644
--- a/src/tether/finetune/resume.py
+++ b/src/tether/finetune/resume.py
@@ -42,7 +42,10 @@ def resolve_resume_checkpoint(output_dir: Path) -> tuple[Path, Path]:
         raise ResumeConfigurationError(
             f"resume checkpoint is missing pretrained_model/train_config.json: {checkpoint}"
         )
-    saved = json.loads(config.read_text())
+    try:
+        saved = json.loads(config.read_text())
+    except (OSError, ValueError) as exc:
+        raise ResumeConfigurationError(f"resume train_config.json is unreadable: {config}") from exc
     if isinstance(saved, dict) and any(
         key in saved for key in ("eval_freq", "use_rabc", "rabc_progress_path", "rabc_kappa", "rabc_epsilon", "rabc_head_mode")
     ):
diff --git a/tests/test_finetune_local_dataset.py b/tests/test_finetune_local_dataset.py
index 6ddaef5a..186a8d4e 100644
--- a/tests/test_finetune_local_dataset.py
+++ b/tests/test_finetune_local_dataset.py
@@ -768,3 +768,21 @@ def test_existing_positional_config_arguments_stay_compatible(tmp_path):
     assert cfg.num_steps == 123
     assert cfg.dataset_root is None
     assert cfg.dataset_manifest_sha256 is None
+
+
+def test_pinned_lerobot_sources_match_installed_qualified_runtime():
+    """The pins are reviewed upstream bytes; check them against a real 0.6.1 install when present."""
+    from importlib import metadata
+
+    from tether._lerobot_version import QUALIFIED_LEROBOT_VERSION
+    from tether.finetune import local_dataset
+
+    try:
+        dist = metadata.distribution("lerobot")
+    except metadata.PackageNotFoundError:
+        pytest.skip("lerobot is not installed")
+    if dist.version != QUALIFIED_LEROBOT_VERSION:
+        pytest.skip(f"lerobot {dist.version} is not the qualified {QUALIFIED_LEROBOT_VERSION}")
+    pins = {**local_dataset.WRITER_FILES, **local_dataset.NORMALIZATION_FILES}
+    actual = {name: hashlib.sha256(Path(dist.locate_file(name)).read_bytes()).hexdigest() for name in pins}
+    assert actual == pins
diff --git a/tests/test_finetune_resume_command.py b/tests/test_finetune_resume_command.py
index e6d9ea4b..285fe2ac 100644
--- a/tests/test_finetune_resume_command.py
+++ b/tests/test_finetune_resume_command.py
@@ -56,3 +56,11 @@ def test_resume_refuses_legacy_lerobot_config(tmp_path):
     with pytest.raises(ResumeConfigurationError, match="cross-version-resume-unsupported") as error:
         build_resume_command(tmp_path)
     assert error.value.code == "cross-version-resume-unsupported"
+
+
+def test_resume_refuses_unreadable_train_config(tmp_path):
+    model = tmp_path / "checkpoints" / "000003" / "pretrained_model"
+    model.mkdir(parents=True)
+    (model / "train_config.json").write_text("{not json")
+    with pytest.raises(ResumeConfigurationError, match="unreadable"):
+        build_resume_command(tmp_path)

From 165102a03669c444bc594d350af5c3d16f28d97d Mon Sep 17 00:00:00 2001
From: RomirJ <playindus@gmail.com>
Date: Mon, 12 Oct 2026 12:52:07 -0700
Subject: [PATCH 3/8] Parity vehicles: torch>=2.8 (onnx-diagnostic patches need
 it); LoRA stub shape

The pi0/pi0.5/GR00T vehicles had been changed to torch==2.7.1. Every
onnx-diagnostic 0.9.x release (0.9.0-0.9.3, checked) patches
torch._export.non_strict_utils._constrain_user_specified_dimhint_range,
which exists from torch 2.8.0 and not in 2.7.1, so
torch_export_patches(patch_transformers=True) raises AttributeError before
any export: the paid GPU runs would have failed. Reproduced locally on
torch 2.7.1 + onnx-diagnostic 0.9.3. The vehicles now take torch>=2.8,<2.12
(lerobot 0.6.1's cap); uv resolves torch 2.11.0, transformers 5.3.0, no
pyyaml-include, for linux x86_64 py3.12.

test_lora_argv_parser: the local-dataset stub returns the real
verify_local_export shape (receipt key) that lerobot_v3.verify_local_dataset
reads since #354; three cases failed whenever lerobot was installed.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 scripts/modal_groot_export_parity_pinned.py | 4 ++--
 scripts/modal_pi05_export_parity_pinned.py  | 8 +++++---
 scripts/modal_pi0_export_parity_pinned.py   | 8 +++++---
 tests/test_lora_argv_parser.py              | 3 ++-
 4 files changed, 14 insertions(+), 9 deletions(-)

diff --git a/scripts/modal_groot_export_parity_pinned.py b/scripts/modal_groot_export_parity_pinned.py
index 380efe41..1edf1322 100644
--- a/scripts/modal_groot_export_parity_pinned.py
+++ b/scripts/modal_groot_export_parity_pinned.py
@@ -63,8 +63,8 @@ def _hf_secret():
     modal.Image.debian_slim(python_version="3.12")
     .apt_install("git")
     .pip_install(
-        "torch==2.7.1",
-        "torchvision==0.22.1",
+        # torch>=2.8: onnx-diagnostic 0.9.x torch_export_patches needs it (see pi0 vehicle).
+        "torch>=2.8,<2.12",
         "safetensors>=0.4.0",
         "huggingface_hub",
         # transformers==5.3.0 EXACTLY: the monolithic exporter refuses
diff --git a/scripts/modal_pi05_export_parity_pinned.py b/scripts/modal_pi05_export_parity_pinned.py
index c5c68c99..527c1e4f 100644
--- a/scripts/modal_pi05_export_parity_pinned.py
+++ b/scripts/modal_pi05_export_parity_pinned.py
@@ -61,9 +61,11 @@ def _hf_secret():
     modal.Image.debian_slim(python_version="3.12")
     .apt_install("git")
     .pip_install(
-        # Qualify the same torch/vision runtime as the CPU probes.
-        "torch==2.7.1",
-        "torchvision==0.22.1",
+        # torch>=2.8: every onnx-diagnostic 0.9.x patches
+        # torch._export.non_strict_utils._constrain_user_specified_dimhint_range,
+        # which torch 2.7.1 lacks, so torch_export_patches() raises before export.
+        # lerobot 0.6.1 caps torch <2.12. (Studio's 2.7.1 pin is an MPS eval pin.)
+        "torch>=2.8,<2.12",
         "lerobot==0.6.1",
         "num2words",
         "diffusers==0.39.0",
diff --git a/scripts/modal_pi0_export_parity_pinned.py b/scripts/modal_pi0_export_parity_pinned.py
index 18c39430..61ec89c5 100644
--- a/scripts/modal_pi0_export_parity_pinned.py
+++ b/scripts/modal_pi0_export_parity_pinned.py
@@ -53,9 +53,11 @@ def _hf_secret():
     modal.Image.debian_slim(python_version="3.12")
     .apt_install("git")
     .pip_install(
-        # Qualify the same torch/vision runtime as the CPU probes.
-        "torch==2.7.1",
-        "torchvision==0.22.1",
+        # torch>=2.8: every onnx-diagnostic 0.9.x patches
+        # torch._export.non_strict_utils._constrain_user_specified_dimhint_range,
+        # which torch 2.7.1 lacks, so torch_export_patches() raises before export.
+        # lerobot 0.6.1 caps torch <2.12. (Studio's 2.7.1 pin is an MPS eval pin.)
+        "torch>=2.8,<2.12",
         "lerobot==0.6.1",
         "num2words",
         "diffusers==0.39.0",
diff --git a/tests/test_lora_argv_parser.py b/tests/test_lora_argv_parser.py
index 5e60b68c..53993adb 100644
--- a/tests/test_lora_argv_parser.py
+++ b/tests/test_lora_argv_parser.py
@@ -80,7 +80,8 @@ def _config(monkeypatch, tmp_path, rank, dataset="remote", resume=False):
         def verified_export(actual_root, actual_digest):
             assert actual_root == root
             assert actual_digest == cfg.dataset_manifest_sha256
-            return snapshot
+            # Real verify_local_export shape (lerobot_v3.verify_local_dataset reads the receipt).
+            return {**snapshot, "receipt": {"artifacts": []}, "info": {}}
 
         monkeypatch.setattr(admission, "verify_local_export", verified_export)
         monkeypatch.setattr(schema, "check_schema", lambda cfg: SimpleNamespace(severity="ok"))

From b2d447f2cb6d1841c0bbfaa97c0891e5b95595a5 Mon Sep 17 00:00:00 2001
From: RomirJ <playindus@gmail.com>
Date: Mon, 12 Oct 2026 13:07:08 -0700
Subject: [PATCH 4/8] Monolithic export: refuse torch<2.8; make dispatch test
 deterministic

onnx-diagnostic 0.8.11 and 0.9.0-0.9.3 patch
torch._export.non_strict_utils._constrain_user_specified_dimhint_range
on entering torch_export_patches(); the symbol first exists in torch
2.8.0. Reproduced: entering the patches fails on torch 2.7.1 with
AttributeError and succeeds on 2.8.0 and 2.11.0. _require_monolithic_deps
admitted torch 2.7.1, so every pi0/pi0.5/SmolVLA/GR00T export died mid
export instead of being refused. The gate now lists torch>=2.8 as a
missing dependency, and the [monolithic] extra declares the same floor
(the jetson extra is left alone: it does not export locally).

test_dispatch_by_model_id called the real SmolVLA exporter and expected
an ImportError, so it started a full export whenever the extras were
installed and failed on any torch<2.8 interpreter that had them. It now
stubs the family exporters and asserts routing for smolvla/pi0/pi05.
New test for the torch floor; removing the check fails it.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 pyproject.toml                      |  3 +++
 src/tether/exporters/monolithic.py  | 18 +++++++++++++++
 tests/test_cli_export_end_to_end.py | 36 ++++++++++++++++++++++++-----
 3 files changed, 51 insertions(+), 6 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 3c3d1f99..948ebc61 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -167,6 +167,9 @@ monolithic = [
     "lerobot==0.6.1; python_version >= '3.12'",
     "draccus==0.11.6; python_version >= '3.12'",
     "transformers==5.3.0",
+    # onnx-diagnostic's export patches reference a torch 2.8+ symbol;
+    # _require_monolithic_deps refuses older torch with the same floor.
+    "torch>=2.8",
     "onnx-diagnostic>=0.9",
     "onnxscript>=0.1",
     "optree",
diff --git a/src/tether/exporters/monolithic.py b/src/tether/exporters/monolithic.py
index 509ba9a0..6c6d8e40 100644
--- a/src/tether/exporters/monolithic.py
+++ b/src/tether/exporters/monolithic.py
@@ -31,6 +31,8 @@
 from pathlib import Path
 from typing import Any
 
+MIN_EXPORT_TORCH = (2, 8)
+
 logger = logging.getLogger(__name__)
 
 
@@ -168,6 +170,22 @@ def _require_monolithic_deps() -> None:
     except ImportError as e:
         missing.append(f"transformers==5.3.0 ({e})")
 
+    # onnx-diagnostic 0.8.11 and 0.9.0-0.9.3 unconditionally patch
+    # torch._export.non_strict_utils._constrain_user_specified_dimhint_range,
+    # which first exists in torch 2.8.0. On older torch every pi0/pi0.5/
+    # SmolVLA/GR00T export dies with an AttributeError on entering
+    # torch_export_patches(); refuse here instead.
+    try:
+        import torch
+        major_minor = tuple(int(part) for part in str(torch.__version__).split(".")[:2])
+        if major_minor < MIN_EXPORT_TORCH:
+            raise ImportError(
+                f"torch {torch.__version__} detected; onnx-diagnostic's export "
+                "patches need torch>=2.8"
+            )
+    except (ImportError, ValueError) as e:
+        missing.append(f"torch>=2.8 ({e})")
+
     try:
         __import__("lerobot")
         try:
diff --git a/tests/test_cli_export_end_to_end.py b/tests/test_cli_export_end_to_end.py
index e1ead410..73e09234 100644
--- a/tests/test_cli_export_end_to_end.py
+++ b/tests/test_cli_export_end_to_end.py
@@ -34,16 +34,40 @@ def test_monolithic_module_importable():
     assert hasattr(monolithic, "apply_export_patches")
 
 
-def test_dispatch_by_model_id(monkeypatch):
-    """`export_monolithic` picks the right backend from the model_id."""
+@pytest.mark.parametrize("model_id,family", [
+    ("lerobot/smolvla_base", "smolvla"),
+    ("lerobot/pi0_base", "pi0"),
+    ("lerobot/pi05_base", "pi05"),
+])
+def test_dispatch_by_model_id(monkeypatch, model_id, family):
+    """`export_monolithic` picks the right backend from the model_id.
+
+    The family exporters are stubbed: calling the real one starts a full
+    export whenever the [monolithic] extras happen to be installed, which
+    made this test pass or fail depending on the interpreter.
+    """
     from tether.exporters import monolithic
 
     monkeypatch.setattr(monolithic, "_model_type_from_hub_config", lambda _: None)
-    with pytest.raises(ImportError, match="Missing dependencies|monolithic"):
-        # Wrong transformers version or missing deps -> ImportError
-        monolithic.export_monolithic(
-            "lerobot/smolvla_base", "/tmp/should_not_run",
+    called = []
+    for name in ("smolvla", "pi0", "pi05", "gr00t"):
+        monkeypatch.setattr(
+            monolithic, f"export_{name}_monolithic",
+            lambda *a, _n=name, **k: called.append(_n) or {},
         )
+    monolithic.export_monolithic(model_id, "/tmp/should_not_run")
+    assert called == [family]
+
+
+def test_dep_check_refuses_torch_without_export_patch_symbol(monkeypatch):
+    """onnx-diagnostic 0.9.x patches a torch 2.8+ symbol on entry, so the
+    gate must refuse torch<2.8 instead of crashing mid-export."""
+    from tether.exporters import monolithic
+    import torch
+
+    monkeypatch.setattr(torch, "__version__", "2.7.1")
+    with pytest.raises(ImportError, match=r"torch>=2\.8 \(torch 2\.7\.1 detected"):
+        monolithic._require_monolithic_deps()
 
 
 def test_unsupported_model_type_raises(monkeypatch):

From 5fb92df22c51c627012216c3fd56f08b75fc30e5 Mon Sep 17 00:00:00 2001
From: RomirJ <playindus@gmail.com>
Date: Mon, 12 Oct 2026 14:41:09 -0700
Subject: [PATCH 5/8] Port patched export paths to LeRobot 0.6.1 shared VLA
 helpers

LeRobot 0.6.1 removed PI0Pytorch/PI05Pytorch._prepare_attention_masks_4d
(now lerobot.policies.common.vla_utils.prepare_attention_masks_4d), which
broke the pi0 GPU parity export. Port every patched prefix/denoise path
(pi0, pi0.5 monolithic, decomposed, SnapFlow pi0/pi0.5/state-out, training
adapters, standalone Modal scripts) to the shared helper. Drop the 0.5.1
embed_image sqrt(hidden_size) shim (0.6.1 native no longer scales). SnapFlow
pi0 clones the prefix cache like native; SnapFlow export freezes cache
updates and forwards explicit state for state-out students.

Adds tests/test_lerobot06_patched_forward.py: tiny real LeRobot 0.6.1
models, patched outputs vs native unpatched forward at rtol=atol=1e-6, plus
torch.export graphs and a private-API audit. Audit and review notes in
docs/research/verification/lerobot-06-export-port-20261012.md.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../lerobot-06-export-port-20261012.md        | 175 +++++++++
 scripts/modal_layer_pruning_profile.py        |   5 +-
 scripts/modal_pi05_monolithic_export.py       |   7 +-
 scripts/modal_pi0_monolithic_export.py        |   8 +-
 scripts/modal_stateout_distill_stage2.py      |   5 +-
 scripts/modal_stateout_smoke.py               |   5 +-
 src/tether/distill/snapflow_pi0_model.py      |  45 +--
 src/tether/exporters/decomposed.py            |   6 +-
 src/tether/exporters/monolithic.py            |  50 ++-
 .../finetune/backends/snapflow_backend.py     |  12 +-
 tests/test_lerobot06_patched_forward.py       | 343 ++++++++++++++++++
 tests/test_snapflow_backend.py                |   2 -
 12 files changed, 591 insertions(+), 72 deletions(-)
 create mode 100644 docs/research/verification/lerobot-06-export-port-20261012.md
 create mode 100644 tests/test_lerobot06_patched_forward.py

diff --git a/docs/research/verification/lerobot-06-export-port-20261012.md b/docs/research/verification/lerobot-06-export-port-20261012.md
new file mode 100644
index 00000000..644c39ed
--- /dev/null
+++ b/docs/research/verification/lerobot-06-export-port-20261012.md
@@ -0,0 +1,175 @@
+# LeRobot 0.6.1 export port, 2026-10-12
+
+The customer is a Tether user exporting a pi0, pi0.5 or SnapFlow policy with
+LeRobot 0.6.1. The required outcome is that each patched prefix, denoise and
+inference path executes the same math as that version's native forward.
+The checkout started clean on `feat/lerobot-0.6.1` at
+`b2d447f2cb6d1841c0bbfaa97c0891e5b95595a5`, PR #355. Changes are uncommitted.
+The existing failed GPU run is recorded in Studio's
+`docs/research/verification/lerobot-06-parity-20261012.md`; it is reused as
+failure evidence, not as successful parity evidence.
+
+## Source comparison and root cause
+
+Compared the installed 0.5.1 and 0.6.1 wheel sources in
+`/Users/romirjain/dev/studio-wt-lerobot06/.sol-scratch/lerobot06/venv-051d`
+and `venv-061t`, including both `pi0/modeling_pi0.py` and
+`pi05/modeling_pi05.py`. The version-pin commit `d7361f8` made the old
+private-method calls fail. The tiny CPU pi0 regression reproduced the exact
+`AttributeError` before the port.
+
+0.5.1's `_prepare_attention_masks_4d` at pi0 line 623 and pi0.5 line 619
+became `lerobot.policies.common.vla_utils.prepare_attention_masks_4d` at
+0.6.1 line 93. Both modeling modules import and call that shared function.
+This is extraction of duplicated VLA utilities. The default computation is
+unchanged: add the singleton head dimension, then select `0.0` or
+`OPENPI_ATTENTION_MASK_VALUE` with `torch.where`. The optional `dtype`
+conversion in 0.6.1 only runs when requested. Tether uses the same default
+as native LeRobot, including float32 additive masks.
+
+Two other numerical changes matter:
+
+- Both native denoise methods now use `clone_past_key_values`, a shared
+  tensor-clone helper, instead of `copy.deepcopy`. The prefix cache remains
+  unchanged between steps. Teacher export retains its equivalent frozen
+  cache update. SnapFlow export now freezes updates too, because graph
+  tracing can evaluate a single-step forward repeatedly. SnapFlow pi0
+  training uses the tensor-clone helper to preserve both the prefix cache
+  and gradients; pi0.5 retains its existing detached training-cache copy.
+- Both native image embedding methods removed the 0.5.1 multiplication by
+  `sqrt(hidden_size)`. Tether's pi0 export shim still multiplied, producing
+  a maximum action difference of 0.5559507608 in the tiny three-step fixture.
+  The shared exporter now uses the native image embedding unchanged, and
+  the standalone export scripts no longer add that factor.
+
+0.6.1 also extracts the sampling loop into
+`lerobot.policies.common.flow_matching.euler_integrate`. Its time schedule
+and arithmetic remain `dt=-1/num_steps`, `t=1+step*dt`, `x=x+dt*v`.
+The decomposed baked wrapper already performs those same operations.
+
+## Ported paths
+
+- pi0 and pi0.5 monolithic teacher denoise patches.
+- Decomposed pi0.5 prefix, per-step expert and baked Euler expert, including
+  SnapFlow and state-out students. Student loading installs the same
+  export-friendly denoise patch as monolithic export.
+- SnapFlow pi0, pi0.5 and state-out denoise/prefix/inference overrides.
+- SnapFlow monolithic export for both families, including explicit state
+  forwarding for the state-out variant.
+- Both SnapFlow training-prefix adapters.
+- Standalone pi0/pi0.5 monolithic export scripts, layer-pruning profile,
+  state-out smoke and state-out stage-two distillation. Their LeRobot pins
+  now match the 0.6.1 helper they call. These Modal scripts were inspected
+  and syntax-checked, not executed on GPU.
+
+## Complete private LeRobot call audit
+
+Searched `src/` and `scripts/` for private calls and imports, followed the
+receiver types, and checked each owner against the installed 0.6.1 source.
+The executable regression also checks the surviving signatures and rejects
+removed-mask calls or new private LeRobot imports across those directories.
+
+| Owner and private symbol | 0.6.1 status | Source and disposition |
+| --- | --- | --- |
+| `PI0Pytorch._prepare_attention_masks_4d` | Removed; all callers ported | Native shared-helper calls at `modeling_pi0.py:616,677,727` |
+| `PI05Pytorch._prepare_attention_masks_4d` | Removed; all callers ported | Native shared-helper calls at `modeling_pi05.py:586,644,692` |
+| `PI0Pytorch._apply_checkpoint` | Present | `modeling_pi0.py:466`, `(self, func, *args, **kwargs)` |
+| `PI05Pytorch._apply_checkpoint` | Present | `modeling_pi05.py:462`, same signature |
+| `PI0Policy._preprocess_images` | Present | `modeling_pi0.py:983`, `(self, batch)` returning image and mask lists |
+| `PI05Policy._preprocess_images` | Present | `modeling_pi05.py:952`, same signature |
+
+There are no underscore-prefixed imports from LeRobot in these directories.
+`config._attn_implementation` belongs to Transformers, is present, and is
+assigned by LeRobot 0.6.1 itself. `lerobot.policies.pi_gemma` and its
+`create_causal_mask` still exist. Python `__class__` and cache `__dict__`
+accesses are not private LeRobot callable symbols.
+
+The original broken pi0 call came from `768a536d`, pi0.5 from `6ffeeff5`,
+decomposed prefix from `bd0f7738`, and SnapFlow subclass from `0be7388f`.
+The separate legacy import `lerobot.common.policies...` in
+`scripts/modal_test_export.py` predates this migration and remains outside
+these patched export paths.
+
+## CPU verification
+
+`tests/test_lerobot06_patched_forward.py` creates real random LeRobot models
+with two language/expert layers, width 32, eight attention heads, a two-layer
+SigLIP vision encoder, vocabulary 64, 16x16 images and a three-action chunk.
+Only constructor configuration sizes are replaced. Embeddings, attention,
+normalization, projections, caches, losses and integration use real library
+code. No checkpoint download or large weights are involved.
+
+Native reference outputs are captured before installing export patches.
+Tests compare native full training-forward losses, denoise velocities and
+three-step teacher inference. SnapFlow checks zero initialization and a
+nonzero learned target-time correction against unpatched LeRobot forward
+methods with the student's intended suffix embedding. State-out includes
+its explicit state token. Both training adapters are exercised. The
+real decomposed teacher and student wrappers compare prefix caches,
+per-step velocities and baked integration against native outputs. Their
+exported prefix, per-step and baked graphs are executed too. Torch graphs for steps
+and full image/language-to-action inference are executed and compared too.
+Every float32 comparison uses `rtol=1e-6`, `atol=1e-6`, with masked cameras
+and padded language tokens in a batch of two. Subprocesses isolate global
+export patches from the rest of pytest.
+
+The requested 0.6.1 environment's actual interpreter is
+`/Users/romirjain/dev/studio-wt-lerobot06/.sol-scratch/lerobot06/venv-061t/bin/python`.
+It has Python 3.12.12, LeRobot 0.6.1, Transformers 5.3.0 and Torch 2.7.1.
+The CPU Torch graphs are diagnostic evidence; production monolithic export
+still correctly requires Torch >=2.8. No new GPU/ONNX Runtime acceptance
+receipt was produced, and existing qualification verdicts are unchanged.
+
+```bash
+PYTHONPATH="$PWD/src" \
+  /Users/romirjain/dev/studio-wt-lerobot06/.sol-scratch/lerobot06/venv-061t/bin/python \
+  -m pytest -q tests/test_lerobot06_patched_forward.py \
+  tests/test_snapflow_backend.py tests/test_monolithic_causal_mask_shim.py
+
+PYTHONPATH="$PWD/src:/Users/romirjain/dev/.sol-out/tether-mcpdeps:/Users/romirjain/dev/.sol-out/tether-asyncdeps" \
+  /Users/romirjain/dev/studio-wt-s11/.sol-scratch/venv/bin/python -m pytest
+```
+
+The final focused run in `venv-061t` passed: **48 passed in 71.20 seconds**,
+including all seven new native/graph/private-API cases, the existing SnapFlow
+backend tests and causal-mask tests. Raw output is
+`/tmp/tether-lerobot06-final-focused.log`.
+
+The full requested suite ran in 206.12 seconds: **4,169 passed, 133 skipped,
+21 failed and 10 errors**. All 31 failures/errors are sandbox-denied TCP
+socket binds: two fake HTTP servers and 29 ZeroMQ cases. Each reports
+`Operation not permitted`; there were no other failures. The zero-failure
+full-suite target therefore requires an environment permitted to bind local
+TCP sockets. No test was weakened or skipped to conceal those results.
+Raw output is `/tmp/tether-lerobot06-full-suite.log`.
+
+After the final loader and test changes, the affected tests in the S11
+interpreter passed: **56 passed, 11 skipped**. LeRobot-specific tests skip
+there because that environment lacks LeRobot; they execute in `venv-061t`.
+The affected core exporter/distillation files and new regression pass Ruff,
+and all edited Python files pass Ruff's undefined-name check. `git diff
+--check` is clean.
+
+## Adversarial review (Claude, 2026-10-12)
+
+- Line-by-line against the LeRobot 0.6.1 wheel (`venv-061t`): `vla_utils.prepare_attention_masks_4d`
+  (`vla_utils.py:93-102`) is called with no `dtype`, as native does at `modeling_pi0.py:677,727`
+  and `modeling_pi05.py:692`. The patched pi0/pi0.5 denoise steps (`monolithic.py` pi0 patch,
+  `_apply_pi05_denoise_step_patch`) match native `denoise_step` (`modeling_pi0.py:705-743`,
+  `modeling_pi05.py:671-708`) term by term; the two deliberate differences are (a) `F.pad`+AND
+  instead of `torch.cat` for the 2D mask, equal by construction when no pad deficit, and (b) a
+  frozen `DynamicLayer.update` instead of `clone_past_key_values`; both feed attention
+  `cat(prefix_kv, suffix_kv)` and leave the prefix cache unchanged. The decomposed prefix wrapper
+  matches native `sample_actions` prefix (`modeling_pi05.py:640-656`). 0.6.1 `embed_image`
+  (`modeling_pi0.py:287-296`) has no `sqrt(hidden_size)` factor (0.5.1 had it at `:446`), so
+  dropping Tether's shim is correct. `euler_integrate` (`flow_matching.py:93-120`) is
+  `dt=-1/n, x+=dt*v`. VERIFIED.
+- The CPU references are native: `native_step = cls.denoise_step`, `expected_*` from a
+  `copy.deepcopy` of the model, all computed before `apply_export_patches()` and the SnapFlow
+  installers run. SnapFlow references call the unpatched class `denoise_step` with only the
+  student's suffix embedding substituted. VERIFIED (`tests/test_lerobot06_patched_forward.py:136-232`).
+- Mutation checks (file restored by copy, shasum `7d653f5b...aef49` equal before and after each):
+  M1 disable the denoise-phase cache freeze in the pi0 patch: 6 of 7 cases fail (pi0: 3-step
+  actions max abs 0.118 at line 249). M2 make the pi0.5 patch ignore the prefix pad mask: pi05
+  and decomposed fail (velocity max abs 0.012 at line 244). Both mutations killed. VERIFIED.
+- Re-run after review: 48 passed in 77.7 s (`venv-061t`). VERIFIED.
diff --git a/scripts/modal_layer_pruning_profile.py b/scripts/modal_layer_pruning_profile.py
index 024cbd73..f9f45d37 100644
--- a/scripts/modal_layer_pruning_profile.py
+++ b/scripts/modal_layer_pruning_profile.py
@@ -65,7 +65,7 @@ def _build_bust() -> str:
     modal.Image.debian_slim(python_version="3.12")
     .apt_install("git")
     .pip_install(
-        "lerobot==0.5.1",
+        "lerobot==0.6.1",
         "transformers==5.3.0",
         "num2words",
         "safetensors>=0.4.0",
@@ -164,6 +164,7 @@ def hook(_module, _inp, output):
                 images = [img, img, img]
                 img_masks = [mask, mask, mask]
                 from lerobot.policies.pi05.modeling_pi05 import make_att_2d_masks
+                from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
                 prefix_embs, prefix_pad_masks, prefix_att_masks = (
                     policy.model.embed_prefix(
                         images, img_masks, lang_tokens, lang_masks,
@@ -172,7 +173,7 @@ def hook(_module, _inp, output):
                 prefix_att_2d = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
                 prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
                 prefix_att_2d_4d = (
-                    policy.model._prepare_attention_masks_4d(prefix_att_2d)
+                    prepare_attention_masks_4d(prefix_att_2d)
                 )
                 language_model.config._attn_implementation = "eager"
                 _ = policy.model.paligemma_with_expert.forward(
diff --git a/scripts/modal_pi05_monolithic_export.py b/scripts/modal_pi05_monolithic_export.py
index b8222245..efa15b6f 100644
--- a/scripts/modal_pi05_monolithic_export.py
+++ b/scripts/modal_pi05_monolithic_export.py
@@ -38,7 +38,7 @@ def _hf_secret():
     modal.Image.debian_slim(python_version="3.12")
     .pip_install(
         # Install lerobot first — it pins torch>=2.7 and hub>=1.0
-        "lerobot==0.5.1",
+        "lerobot==0.6.1",
         "num2words",
         "safetensors>=0.4.0",
         "onnx>=1.16",
@@ -165,7 +165,6 @@ def _patched_embed_image(self, image):
             features = out.pooler_output
         else:
             features = out
-        features = features * self.paligemma.config.text_config.hidden_size ** 0.5
         if features.dtype != out_dtype:
             features = features.to(out_dtype)
         return features
@@ -203,6 +202,7 @@ def _ccm_shim(*args, **kwargs):
     import torch.nn.functional as _F
     from lerobot.policies.pi05 import modeling_pi05 as _mp0
     from lerobot.policies.pi05.modeling_pi05 import make_att_2d_masks as _make_att_2d_masks
+    from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
 
     _patched_called = [0]
 
@@ -247,7 +247,7 @@ def _patched_denoise_step(self, prefix_pad_masks, past_key_values, x_t, timestep
         prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
         position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
 
-        full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
+        full_att_2d_masks_4d = prepare_attention_masks_4d(full_att_2d_masks)
         self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"
 
         outputs_embeds, _ = self.paligemma_with_expert.forward(
@@ -510,7 +510,6 @@ def _patched_embed_image(self, image):
             image = image.to(torch.float32)
         out = self.paligemma.model.get_image_features(image)
         features = out.pooler_output if hasattr(out, "pooler_output") else out
-        features = features * self.paligemma.config.text_config.hidden_size ** 0.5
         if features.dtype != out_dtype:
             features = features.to(out_dtype)
         return features
diff --git a/scripts/modal_pi0_monolithic_export.py b/scripts/modal_pi0_monolithic_export.py
index 34df8896..09aefd64 100644
--- a/scripts/modal_pi0_monolithic_export.py
+++ b/scripts/modal_pi0_monolithic_export.py
@@ -48,7 +48,7 @@ def _hf_secret():
     modal.Image.debian_slim(python_version="3.12")
     .pip_install(
         # Install lerobot first — it pins torch>=2.7 and hub>=1.0
-        "lerobot==0.5.1",
+        "lerobot==0.6.1",
         "num2words",
         "safetensors>=0.4.0",
         "onnx>=1.16",
@@ -175,7 +175,6 @@ def _patched_embed_image(self, image):
             features = out.pooler_output
         else:
             features = out
-        features = features * self.paligemma.config.text_config.hidden_size ** 0.5
         if features.dtype != out_dtype:
             features = features.to(out_dtype)
         return features
@@ -213,6 +212,7 @@ def _ccm_shim(*args, **kwargs):
     import torch.nn.functional as _F
     from lerobot.policies.pi0 import modeling_pi0 as _mp0
     from lerobot.policies.pi0.modeling_pi0 import make_att_2d_masks as _make_att_2d_masks
+    from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
 
     _patched_called = [0]
 
@@ -256,7 +256,7 @@ def _patched_denoise_step(self, state, prefix_pad_masks, past_key_values, x_t, t
         prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
         position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
 
-        full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
+        full_att_2d_masks_4d = prepare_attention_masks_4d(full_att_2d_masks)
         self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"
 
         outputs_embeds, _ = self.paligemma_with_expert.forward(
@@ -519,7 +519,6 @@ def _patched_embed_image(self, image):
             image = image.to(torch.float32)
         out = self.paligemma.model.get_image_features(image)
         features = out.pooler_output if hasattr(out, "pooler_output") else out
-        features = features * self.paligemma.config.text_config.hidden_size ** 0.5
         if features.dtype != out_dtype:
             features = features.to(out_dtype)
         return features
@@ -750,7 +749,6 @@ def _patched_embed_image(self, image):
             image = image.to(torch.float32)
         out = self.paligemma.model.get_image_features(image)
         features = out.pooler_output if hasattr(out, "pooler_output") else out
-        features = features * self.paligemma.config.text_config.hidden_size ** 0.5
         if features.dtype != out_dtype:
             features = features.to(out_dtype)
         return features
diff --git a/scripts/modal_stateout_distill_stage2.py b/scripts/modal_stateout_distill_stage2.py
index 2a5228a9..f2e3d357 100644
--- a/scripts/modal_stateout_distill_stage2.py
+++ b/scripts/modal_stateout_distill_stage2.py
@@ -68,7 +68,7 @@ def _bust():
     modal.Image.debian_slim(python_version="3.12")
     .apt_install("git", "ffmpeg")
     .pip_install(
-        "lerobot==0.5.1",
+        "lerobot==0.6.1",
         "transformers==5.3.0",
         "num2words",
         "safetensors>=0.4.0",
@@ -111,6 +111,7 @@ def distill(
     import torch
     import torch.nn.functional as F
     from lerobot.policies.pi05.modeling_pi05 import PI05Policy, PI05Pytorch, make_att_2d_masks
+    from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
     from tether.distill.snapflow_pi0_model import (
         load_snapflow_student,
         enable_snapflow_state_out,
@@ -246,7 +247,7 @@ def distill(
         )
         prefix_att_2d = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
         prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
-        prefix_att_2d_masks_4d = student.model._prepare_attention_masks_4d(prefix_att_2d)
+        prefix_att_2d_masks_4d = prepare_attention_masks_4d(prefix_att_2d)
         student.model.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"
         _, past_key_values = student.model.paligemma_with_expert.forward(
             attention_mask=prefix_att_2d_masks_4d,
diff --git a/scripts/modal_stateout_smoke.py b/scripts/modal_stateout_smoke.py
index 0c2b4974..6b1307cf 100644
--- a/scripts/modal_stateout_smoke.py
+++ b/scripts/modal_stateout_smoke.py
@@ -58,7 +58,7 @@ def _bust():
     modal.Image.debian_slim(python_version="3.12")
     .apt_install("git")
     .pip_install(
-        "lerobot==0.5.1",
+        "lerobot==0.6.1",
         "transformers==5.3.0",
         "num2words",
         "safetensors>=0.4.0",
@@ -167,9 +167,10 @@ def smoke(
             images, img_masks, lang_tokens, lang_masks,
         )
         from lerobot.policies.pi05.modeling_pi05 import make_att_2d_masks
+        from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
         prefix_att_2d = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
         prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
-        prefix_att_2d_masks_4d = policy.model._prepare_attention_masks_4d(prefix_att_2d)
+        prefix_att_2d_masks_4d = prepare_attention_masks_4d(prefix_att_2d)
         policy.model.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"
 
         _, past_key_values = policy.model.paligemma_with_expert.forward(
diff --git a/src/tether/distill/snapflow_pi0_model.py b/src/tether/distill/snapflow_pi0_model.py
index 043f8882..d0a8a45c 100644
--- a/src/tether/distill/snapflow_pi0_model.py
+++ b/src/tether/distill/snapflow_pi0_model.py
@@ -7,11 +7,11 @@
      to a correction vector added to the time channel before the
      action_time MLP. When ``target_time`` is None, behaves exactly like
      the parent (teacher inference path is unaffected).
-  2. ``denoise_step`` does NOT ``copy.deepcopy(past_key_values)`` — the
-     downstream ``paligemma_with_expert.forward`` is called with
-     ``use_cache=False`` so past_kv is read-only; deepcopy is strictly
-     defensive overhead (and breaks on graph-attached tensors during
-     training — pytorch#103001).
+  2. pi0 ``denoise_step`` clones the prefix cache with LeRobot 0.6.1's
+     tensor-clone helper. DynamicCache updates occur even with
+     ``use_cache=False``; cloning preserves the prefix and VLM gradients
+     without ``copy.deepcopy`` on graph-attached tensors. pi0.5 retains
+     its detached cache copy for the existing expert-only training path.
   3. ``denoise_step`` does NOT force-cast ``suffix_out`` to fp32 — uses
      ``self.action_out_proj.weight.dtype`` instead, so bf16 training
      stays bf16 end-to-end.
@@ -150,10 +150,12 @@ def _resolve_snapflow_pi0_class() -> type:
         return _SNAPFLOW_PI0_CLASS
 
     import torch
-    from lerobot.policies.pi0.modeling_pi0 import (
-        PI0Pytorch,
+    from lerobot.policies.pi0.modeling_pi0 import PI0Pytorch
+    from lerobot.policies.common.vla_utils import (
+        clone_past_key_values,
         create_sinusoidal_pos_embedding,
         make_att_2d_masks,
+        prepare_attention_masks_4d,
     )
 
     class SnapFlowPI0Pytorch(PI0Pytorch):
@@ -263,8 +265,9 @@ def denoise_step(
         ):
             """Parent denoise_step minus the deepcopy and minus the fp32 cast.
 
-            - Skips ``copy.deepcopy(past_key_values)``: safe because the
-              downstream forward uses ``use_cache=False`` (read-only pass).
+            - Clones the prefix cache using LeRobot's tensor clone helper.
+              This preserves VLM gradients and prevents DynamicCache updates
+              from changing the shared prefix between velocity evaluations.
             - Skips the hardcoded ``suffix_out.to(fp32)``: casts to
               ``self.action_out_proj.weight.dtype`` instead, so bf16 training
               stays bf16.
@@ -289,9 +292,10 @@ def denoise_step(
             prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
             position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
 
-            full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
+            full_att_2d_masks_4d = prepare_attention_masks_4d(full_att_2d_masks)
             self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
 
+            past_key_values = clone_past_key_values(past_key_values)
             outputs_embeds, _ = self.paligemma_with_expert.forward(
                 attention_mask=full_att_2d_masks_4d,
                 position_ids=position_ids,
@@ -343,7 +347,7 @@ def sample_actions_1step(
             )
             prefix_att_2d = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
             prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
-            prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d)
+            prefix_att_2d_masks_4d = prepare_attention_masks_4d(prefix_att_2d)
             self.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
 
             _, past_key_values = self.paligemma_with_expert.forward(
@@ -392,10 +396,11 @@ def _resolve_snapflow_pi05_class() -> type:
         return _SNAPFLOW_PI05_CLASS
 
     import torch
-    from lerobot.policies.pi05.modeling_pi05 import (
-        PI05Pytorch,
+    from lerobot.policies.pi05.modeling_pi05 import PI05Pytorch
+    from lerobot.policies.common.vla_utils import (
         create_sinusoidal_pos_embedding,
         make_att_2d_masks,
+        prepare_attention_masks_4d,
     )
 
     class SnapFlowPI05Pytorch(PI05Pytorch):
@@ -533,7 +538,7 @@ def denoise_step(
             prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
             position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
 
-            full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
+            full_att_2d_masks_4d = prepare_attention_masks_4d(full_att_2d_masks)
             self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
 
             # Detach past_kv tensors so deepcopy succeeds on graph-attached
@@ -583,7 +588,7 @@ def sample_actions_1step(
             )
             prefix_att_2d = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
             prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
-            prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d)
+            prefix_att_2d_masks_4d = prepare_attention_masks_4d(prefix_att_2d)
             self.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
 
             _, past_key_values = self.paligemma_with_expert.forward(
@@ -641,11 +646,7 @@ def _resolve_snapflow_pi05_state_out_class() -> type:
         return _SNAPFLOW_PI05_STATE_OUT_CLASS
 
     import torch
-    import torch.nn.functional as F
-    from lerobot.policies.pi05.modeling_pi05 import (
-        create_sinusoidal_pos_embedding,
-        make_att_2d_masks,
-    )
+    from lerobot.policies.common.vla_utils import make_att_2d_masks, prepare_attention_masks_4d
 
     parent = _resolve_snapflow_pi05_class()
 
@@ -745,7 +746,7 @@ def denoise_step(  # type: ignore[override]
             prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
             position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
 
-            full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
+            full_att_2d_masks_4d = prepare_attention_masks_4d(full_att_2d_masks)
             self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
 
             past_key_values = _detach_kv_cache_deep(past_key_values)
@@ -794,7 +795,7 @@ def sample_actions_1step(  # type: ignore[override]
             )
             prefix_att_2d = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
             prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
-            prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d)
+            prefix_att_2d_masks_4d = prepare_attention_masks_4d(prefix_att_2d)
             self.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
 
             _, past_key_values = self.paligemma_with_expert.forward(
diff --git a/src/tether/exporters/decomposed.py b/src/tether/exporters/decomposed.py
index bd3e747d..1d9a8ba3 100644
--- a/src/tether/exporters/decomposed.py
+++ b/src/tether/exporters/decomposed.py
@@ -359,6 +359,7 @@ def _load_pi05_policy(
         apply_export_patches,
         _force_eager_attn,
         _apply_pi05_denoise_step_patch,
+        _install_snapflow_export_denoise_step,
     )
 
     apply_export_patches()
@@ -406,6 +407,8 @@ def _load_pi05_policy(
         _gc_disable()
     _force_eager_attn(policy.model)
     _apply_pi05_denoise_step_patch()  # reuses monolithic's F.pad mask fix
+    if student_checkpoint is not None:
+        _install_snapflow_export_denoise_step(policy.model, is_pi05=True)
     logger.info("[decomposed] Loaded in %.1fs", time.time() - t0)
     return policy
 
@@ -789,6 +792,7 @@ def _build_prefix_class():
     import torch
     import torch.nn as nn
     from lerobot.policies.pi05.modeling_pi05 import make_att_2d_masks
+    from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
 
     class _Pi05PrefixWrapper(nn.Module):
         def __init__(self, pi05_model):
@@ -817,7 +821,7 @@ def forward(
             )
             prefix_att_2d = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
             prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
-            prefix_att_2d_masks_4d = self.model._prepare_attention_masks_4d(prefix_att_2d)
+            prefix_att_2d_masks_4d = prepare_attention_masks_4d(prefix_att_2d)
             self.model.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
 
             _, past_key_values = self.model.paligemma_with_expert.forward(
diff --git a/src/tether/exporters/monolithic.py b/src/tether/exporters/monolithic.py
index 6c6d8e40..9a832d52 100644
--- a/src/tether/exporters/monolithic.py
+++ b/src/tether/exporters/monolithic.py
@@ -453,24 +453,9 @@ def _safe_where(condition, x=None, y=None, *args, **kwargs):
         return _orig_where(condition, x, y, *args, **kwargs)
     torch.where = _safe_where
 
-    # pi0-specific: PaliGemmaWithExpertModel.embed_image image_outputs extraction
-    try:
-        from lerobot.policies.pi0 import modeling_pi0
-        import torch as _t
-
-        def _patched_embed_image(self, image):
-            out_dtype = image.dtype
-            if image.dtype != _t.float32:
-                image = image.to(_t.float32)
-            out = self.paligemma.model.get_image_features(image)
-            features = out.pooler_output if hasattr(out, "pooler_output") else out
-            features = features * self.paligemma.config.text_config.hidden_size ** 0.5
-            if features.dtype != out_dtype:
-                features = features.to(out_dtype)
-            return features
-        modeling_pi0.PaliGemmaWithExpertModel.embed_image = _patched_embed_image
-    except ImportError:
-        pass
+    # LeRobot 0.6.1 returns image features in its native embedding scale.
+    # Keep embed_image unchanged: multiplying by sqrt(hidden_size), as the
+    # old 0.5.1 shim did, changes every prefix cache and exported action.
 
     # ===============================================================
     # pi0 cos=1.0 at num_steps=10 — the full fix stack (2026-04-19)
@@ -505,6 +490,7 @@ def _patched_embed_image(self, image):
         from transformers.cache_utils import DynamicLayer as _DL
         from lerobot.policies.pi0 import modeling_pi0 as _mp0
         from lerobot.policies.pi0.modeling_pi0 import make_att_2d_masks as _make_att_2d_masks
+        from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
 
         _orig_layer_update = _DL.update
 
@@ -545,7 +531,7 @@ def _patched_denoise_step(self, state, prefix_pad_masks, past_key_values, x_t, t
             prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
             position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
 
-            full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
+            full_att_2d_masks_4d = prepare_attention_masks_4d(full_att_2d_masks)
             self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"
 
             outputs_embeds, _ = self.paligemma_with_expert.forward(
@@ -1190,6 +1176,7 @@ def _apply_pi05_denoise_step_patch() -> None:
         import torch
         from lerobot.policies.pi05 import modeling_pi05 as _mp05
         from lerobot.policies.pi05.modeling_pi05 import make_att_2d_masks as _make
+        from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
 
         def _patched(self, prefix_pad_masks, past_key_values, x_t, timestep):
             # pi0.5: NO state arg (tokenized into language upstream).
@@ -1218,7 +1205,7 @@ def _patched(self, prefix_pad_masks, past_key_values, x_t, timestep):
             prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
             position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
 
-            full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
+            full_att_2d_masks_4d = prepare_attention_masks_4d(full_att_2d_masks)
             self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"
 
             outputs_embeds, _ = self.paligemma_with_expert.forward(
@@ -1434,12 +1421,14 @@ def forward(
 def _install_snapflow_export_denoise_step(model: Any, *, is_pi05: bool) -> None:
     """Replace SnapFlowPI*Pytorch.denoise_step with an export-friendly variant
     that drops the training-only detach+deepcopy (which doesn't trace
-    through torch.export.export). The 1-step export path calls denoise_step
-    exactly once per forward, so past_kv isn't reused across calls — the
-    defensive copy that matters for multi-step inference isn't needed here.
+    through torch.export.export). Freeze cache updates during the expert
+    pass, as for teacher export: tracing can evaluate even a single-step
+    forward repeatedly, and must preserve the prefix cache each time.
     """
     import torch
 
+    from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
+
     if is_pi05:
         from lerobot.policies.pi05.modeling_pi05 import make_att_2d_masks
     else:
@@ -1450,6 +1439,7 @@ def _export_denoise_step(self, prefix_pad_masks, past_key_values, x_t,
                                  timestep, target_time=None, state=None):
             suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(
                 x_t, timestep, target_time=target_time,
+                **({"state": state} if hasattr(self, "state_proj") else {}),
             )
             suffix_len = suffix_pad_masks.shape[1]
             batch_size = prefix_pad_masks.shape[0]
@@ -1465,7 +1455,7 @@ def _export_denoise_step(self, prefix_pad_masks, past_key_values, x_t,
 
             prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
             position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
-            full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
+            full_att_2d_masks_4d = prepare_attention_masks_4d(full_att_2d_masks)
             self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"
 
             outputs_embeds, _ = self.paligemma_with_expert.forward(
@@ -1499,7 +1489,7 @@ def _export_denoise_step(self, state, prefix_pad_masks, past_key_values,
 
             prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
             position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
-            full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
+            full_att_2d_masks_4d = prepare_attention_masks_4d(full_att_2d_masks)
             self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"
 
             outputs_embeds, _ = self.paligemma_with_expert.forward(
@@ -1517,7 +1507,15 @@ def _export_denoise_step(self, state, prefix_pad_masks, past_key_values,
     # Bind onto the class (the SnapFlow subclass) so the instance picks
     # it up. This overrides the training-time detach+deepcopy variant
     # for the duration of the export process.
-    type(model).denoise_step = _export_denoise_step
+    def _denoise_step_with_flag(self, *args, **kwargs):
+        previous_phase = _denoise_phase[0]
+        _denoise_phase[0] = True
+        try:
+            return _export_denoise_step(self, *args, **kwargs)
+        finally:
+            _denoise_phase[0] = previous_phase
+
+    type(model).denoise_step = _denoise_step_with_flag
 
 
 def export_gr00t_monolithic(
diff --git a/src/tether/finetune/backends/snapflow_backend.py b/src/tether/finetune/backends/snapflow_backend.py
index 1a1c0cc1..80951f87 100644
--- a/src/tether/finetune/backends/snapflow_backend.py
+++ b/src/tether/finetune/backends/snapflow_backend.py
@@ -560,6 +560,7 @@ def _build_pi05_adapters(teacher: Any, student: Any):
     """
     import torch
     from lerobot.policies.pi05.modeling_pi05 import make_att_2d_masks
+    from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
 
     from lerobot.utils.constants import (
         OBS_LANGUAGE_ATTENTION_MASK,
@@ -577,7 +578,7 @@ def _build_prefix_cache_pi05(policy, obs_kwargs):
         )
         prefix_att_2d = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
         prefix_pos = torch.cumsum(prefix_pad_masks, dim=1) - 1
-        prefix_att_4d = m._prepare_attention_masks_4d(prefix_att_2d)
+        prefix_att_4d = prepare_attention_masks_4d(prefix_att_2d)
         m.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"
         m.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"
         _, past_kv = m.paligemma_with_expert.forward(
@@ -681,6 +682,7 @@ def _build_pi_family_adapters(
     import torch
     import torch.nn.functional as F
     from lerobot.policies.pi0.modeling_pi0 import make_att_2d_masks
+    from lerobot.policies.common.vla_utils import prepare_attention_masks_4d
     from lerobot.utils.constants import (
         OBS_LANGUAGE_ATTENTION_MASK,
         OBS_LANGUAGE_TOKENS,
@@ -718,7 +720,7 @@ def _build_prefix_cache(policy, obs_kwargs):
         )
         prefix_att_2d = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
         prefix_pos = torch.cumsum(prefix_pad_masks, dim=1) - 1
-        prefix_att_4d = m._prepare_attention_masks_4d(prefix_att_2d)
+        prefix_att_4d = prepare_attention_masks_4d(prefix_att_2d)
         # Mirror lerobot's sample_actions trick: force eager attention
         # before the prefix forward so bf16 models don't hit the SDPA
         # dtype-mismatch-on-bias error (modeling_pi0.py:844).
@@ -731,10 +733,8 @@ def _build_prefix_cache(policy, obs_kwargs):
             inputs_embeds=[prefix_embs, None],
             use_cache=True,
         )
-        # SnapFlowPI0Pytorch.denoise_step skips the `copy.deepcopy(past_kv)`
-        # that the stock lerobot path does (modeling_pi0.py:918), so we no
-        # longer need to detach past_kv tensors. Student path now keeps
-        # past_kv graph-attached → VLM receives gradients.
+        # SnapFlow pi0 uses LeRobot's tensor clone helper, so the student
+        # cache remains graph-attached and VLM gradients can flow.
         return past_kv, prefix_pad_masks, state
 
     def _run_denoise_step(policy, x, t, obs_kwargs, target_time=None):
diff --git a/tests/test_lerobot06_patched_forward.py b/tests/test_lerobot06_patched_forward.py
new file mode 100644
index 00000000..567621f5
--- /dev/null
+++ b/tests/test_lerobot06_patched_forward.py
@@ -0,0 +1,343 @@
+"""Real, tiny LeRobot forwards; process isolation keeps export patches out of other tests."""
+
+from __future__ import annotations
+
+import importlib.metadata
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+
+@pytest.mark.parametrize(
+    "path",
+    [
+        "pi0",
+        "pi05",
+        "decomposed",
+        "snapflow_pi0",
+        "snapflow_pi05",
+        "snapflow_state_out",
+        "private_api",
+    ],
+)
+def test_patched_forward_matches_lerobot06(path):
+    try:
+        version = importlib.metadata.version("lerobot")
+    except importlib.metadata.PackageNotFoundError:
+        pytest.skip("requires the LeRobot 0.6.1 native extra")
+    if version != "0.6.1":
+        pytest.skip("this regression exercises LeRobot 0.6.1")
+    result = subprocess.run(
+        [sys.executable, str(Path(__file__).resolve()), path],
+        capture_output=True,
+        text=True,
+        timeout=120,
+    )
+    assert result.returncode == 0, result.stdout + result.stderr
+
+
+def _check(path):
+    import copy
+    import torch
+    from unittest.mock import patch
+    from lerobot.policies.pi0 import modeling_pi0 as pi0
+    from lerobot.policies.pi05 import modeling_pi05 as pi05
+    from lerobot.policies.common.vla_utils import make_att_2d_masks, prepare_attention_masks_4d
+    from tether.exporters import monolithic, decomposed
+    from tether.distill import snapflow_pi0_model as snapflow
+
+    if path == "private_api":
+        import ast
+        import inspect
+
+        for owner in (pi0.PI0Pytorch, pi05.PI05Pytorch):
+            assert not hasattr(owner, "_prepare_attention_masks_4d")
+            assert (
+                str(inspect.signature(owner._apply_checkpoint)) == "(self, func, *args, **kwargs)"
+            )
+        for owner in (pi0.PI0Policy, pi05.PI05Policy):
+            assert list(inspect.signature(owner._preprocess_images).parameters) == ["self", "batch"]
+        root = Path(__file__).resolve().parents[1]
+        for directory in (root / "src", root / "scripts"):
+            for source in directory.rglob("*.py"):
+                tree = ast.parse(source.read_text())
+                for node in ast.walk(tree):
+                    if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute):
+                        assert node.func.attr != "_prepare_attention_masks_4d", str(source)
+                    if isinstance(node, ast.ImportFrom) and (node.module or "").startswith(
+                        "lerobot"
+                    ):
+                        assert all(not name.name.startswith("_") for name in node.names), str(
+                            source
+                        )
+        return
+
+    torch.set_num_threads(1)
+    torch.manual_seed(123)
+    module = pi0 if path.endswith("pi0") or path == "pi0" else pi05
+    cls = module.PI0Pytorch if module is pi0 else module.PI05Pytorch
+    config_cls = module.PI0Config if module is pi0 else module.PI05Config
+    # Shrink constructor configurations only. Every embedding, attention, norm,
+    # velocity, cache and Euler computation remains the real LeRobot code.
+    original_pali = module.PaliGemmaForConditionalGenerationWithPiGemma
+    original_expert = module.PiGemmaForCausalLM
+
+    def tiny_pali(config):
+        config.text_config.vocab_size = 64
+        config.vision_config.hidden_size = 32
+        config.vision_config.intermediate_size = 64
+        config.vision_config.num_hidden_layers = 2
+        config.vision_config.num_attention_heads = 4
+        config.vision_config.patch_size = 4
+        config.vision_config.projection_dim = 32
+        return original_pali(config=config)
+
+    def tiny_expert(config):
+        config.vocab_size = 64
+        return original_expert(config=config)
+
+    config = config_cls(
+        device="cpu",
+        dtype="float32",
+        image_resolution=(16, 16),
+        max_action_dim=4,
+        max_state_dim=4,
+        chunk_size=3,
+        n_action_steps=3,
+        compile_model=False,
+    )
+    with (
+        patch.object(
+            module, "get_gemma_config", return_value=module.GemmaConfig(32, 2, 64, 8, 1, 4)
+        ),
+        patch.object(module, "PaliGemmaForConditionalGenerationWithPiGemma", tiny_pali),
+        patch.object(module, "PiGemmaForCausalLM", tiny_expert),
+    ):
+        model = cls(config).eval()
+    images = [torch.randn(2, 3, 16, 16) for _ in range(3)]
+    image_masks = [
+        torch.tensor([True, True]),
+        torch.tensor([True, False]),
+        torch.tensor([False, True]),
+    ]
+    tokens = torch.randint(0, 64, (2, 4))
+    masks = torch.tensor([[True, True, False, False], [True, True, True, False]])
+    state = torch.randn(2, 4)
+    noise = torch.randn(2, 3, 4)
+    actions = torch.randn_like(noise)
+    time = torch.tensor([0.7, 0.3])
+    obs = [images, image_masks, tokens, masks] + ([state] if module is pi0 else [])
+    native = copy.deepcopy(model)
+    native_step = cls.denoise_step
+    with torch.no_grad():
+        expected_loss = native(*obs, actions, noise, time)
+        expected_actions = native.sample_actions(*obs, noise=noise.clone(), num_steps=3)
+        expected_one_step = native.sample_actions(*obs, noise=noise.clone(), num_steps=1)
+        prefix, pad, att = native.embed_prefix(images, image_masks, tokens, masks)
+        _, cache = native.paligemma_with_expert(
+            attention_mask=prepare_attention_masks_4d(make_att_2d_masks(pad, att)),
+            position_ids=pad.cumsum(1) - 1,
+            inputs_embeds=[prefix, None],
+            use_cache=True,
+        )
+        x_t = time[:, None, None] * noise + (1 - time[:, None, None]) * actions
+        step_kw = dict(prefix_pad_masks=pad, past_key_values=cache, x_t=x_t, timestep=time)
+        if module is pi0:
+            step_kw["state"] = state
+        expected_velocity = native_step(native, **step_kw)
+
+        if path.startswith("snapflow"):
+            snapflow.enable_snapflow(model)
+            if path == "snapflow_state_out":
+                snapflow.enable_snapflow_state_out(model)
+            # A zero target-time contribution must reproduce the teacher.
+            step_kw["target_time"] = torch.ones_like(time)
+            if path == "snapflow_state_out":
+                step_kw["state"] = state
+                # The state-out student's embedding is its intentional extension.
+                # Execute the UNPATCHED LeRobot denoise forward with that embedding.
+                ref = copy.deepcopy(model)
+                embed = ref.embed_suffix
+                ref.embed_suffix = lambda x, t: embed(
+                    x, t, target_time=torch.ones_like(t), state=state
+                )
+                expected_velocity = native_step(
+                    ref, **{k: v for k, v in step_kw.items() if k not in ("state", "target_time")}
+                )
+            torch.testing.assert_close(
+                model.denoise_step(**step_kw), expected_velocity, rtol=1e-6, atol=1e-6
+            )
+            import types
+
+            if path == "snapflow_state_out":
+                ref.denoise_step = types.MethodType(native_step, ref)
+                expected_one_step = cls.sample_actions(ref, *obs, noise=noise.clone(), num_steps=1)
+            sample_obs = obs + ([state] if path == "snapflow_state_out" else [])
+            torch.testing.assert_close(
+                model.sample_actions_1step(*sample_obs, noise=noise.clone()),
+                expected_one_step,
+                rtol=1e-6,
+                atol=1e-6,
+            )
+            # Exercise both training-prefix adapters with real model forwards.
+            from types import SimpleNamespace
+            from tether.finetune.backends.snapflow_backend import _build_velocity_adapters
+            from lerobot.utils.constants import (
+                OBS_LANGUAGE_TOKENS,
+                OBS_LANGUAGE_ATTENTION_MASK,
+                OBS_STATE,
+            )
+
+            policy = SimpleNamespace(
+                model=model,
+                config=config,
+                _preprocess_images=lambda batch: (images, image_masks),
+                prepare_state=lambda batch: state,
+            )
+            _, student_velocity = _build_velocity_adapters(
+                policy, policy, "pi0" if module is pi0 else "pi05"
+            )
+            torch.testing.assert_close(
+                student_velocity(
+                    x_t,
+                    time,
+                    target_time=torch.ones_like(time),
+                    **{
+                        OBS_LANGUAGE_TOKENS: tokens,
+                        OBS_LANGUAGE_ATTENTION_MASK: masks,
+                        OBS_STATE: state,
+                    },
+                ),
+                expected_velocity,
+                rtol=1e-6,
+                atol=1e-6,
+            )
+            # Also test a learned target-time correction. A zero-init-only
+            # test would miss an exporter silently dropping target_time.
+            model.target_time_embed_mlp[-1].weight.normal_(std=0.01)
+            model.target_time_embed_mlp[-1].bias.normal_(std=0.01)
+            ref = copy.deepcopy(model)
+            embed = ref.embed_suffix
+            state_kwargs = {"state": state} if path == "snapflow_state_out" else {}
+            ref.embed_suffix = lambda *args: embed(
+                *args, target_time=torch.ones_like(args[-1]), **state_kwargs
+            )
+            ref.denoise_step = types.MethodType(native_step, ref)
+            native_kwargs = {k: v for k, v in step_kw.items() if k != "target_time"}
+            if path == "snapflow_state_out":
+                native_kwargs.pop("state")
+            expected_velocity = native_step(ref, **native_kwargs)
+            expected_loss = cls.forward(ref, *obs, actions, noise, time)
+            expected_one_step = cls.sample_actions(ref, *obs, noise=noise.clone(), num_steps=1)
+            torch.testing.assert_close(
+                model.denoise_step(**step_kw), expected_velocity, rtol=1e-6, atol=1e-6
+            )
+        monolithic.apply_export_patches()
+        if module is pi05:
+            monolithic._apply_pi05_denoise_step_patch()
+        if path.startswith("snapflow"):
+            monolithic._install_snapflow_export_denoise_step(model, is_pi05=module is pi05)
+        step_kw["past_key_values"] = copy.deepcopy(cache)
+        actual_velocity = model.denoise_step(**step_kw)
+        torch.testing.assert_close(actual_velocity, expected_velocity, rtol=1e-6, atol=1e-6)
+        torch.testing.assert_close(
+            (noise - actions - actual_velocity).square(), expected_loss, rtol=1e-6, atol=1e-6
+        )
+        if not path.startswith("snapflow"):
+            torch.testing.assert_close(
+                model.sample_actions(*obs, noise=noise.clone(), num_steps=3),
+                expected_actions,
+                rtol=1e-6,
+                atol=1e-6,
+            )
+        if module is pi05 and path != "pi05":
+            decomposed.PI05_PALIGEMMA_LAYERS = 2
+            prefix_wrapper = decomposed._build_prefix_class()(model)
+            flat = prefix_wrapper(*images, *image_masks, tokens, masks)
+            for actual, layer_value in zip(
+                flat[:-1],
+                [v for layer in cache.layers for v in (layer.keys, layer.values)],
+                strict=True,
+            ):
+                torch.testing.assert_close(actual, layer_value, rtol=1e-6, atol=1e-6)
+            torch.testing.assert_close(flat[-1], pad, rtol=0, atol=0)
+            per_step = decomposed._build_expert_per_step_class()(model)
+            torch.testing.assert_close(
+                per_step(*flat, x_t, time, *([state] if path == "snapflow_state_out" else [])),
+                expected_velocity,
+                rtol=1e-6,
+                atol=1e-6,
+            )
+            n_steps = 1 if path.startswith("snapflow") else 3
+            baked = decomposed._build_expert_class()(model, n_steps)
+            state_args = (state,) if path == "snapflow_state_out" else ()
+            step_args = (*flat, x_t, time, *state_args)
+            baked_args = (*flat, noise, *state_args)
+            expected_baked = expected_one_step if path.startswith("snapflow") else expected_actions
+            torch.testing.assert_close(baked(*baked_args), expected_baked, rtol=1e-6, atol=1e-6)
+            prefix_graph = torch.export.export(
+                prefix_wrapper, (*images, *image_masks, tokens, masks), strict=False
+            ).module()
+            for actual, expected in zip(
+                prefix_graph(*images, *image_masks, tokens, masks), flat, strict=True
+            ):
+                torch.testing.assert_close(actual, expected, rtol=1e-6, atol=1e-6)
+            step_graph = torch.export.export(per_step, step_args, strict=False).module()
+            torch.testing.assert_close(
+                step_graph(*step_args), expected_velocity, rtol=1e-6, atol=1e-6
+            )
+            baked_graph = torch.export.export(baked, baked_args, strict=False).module()
+            torch.testing.assert_close(
+                baked_graph(*baked_args), expected_baked, rtol=1e-6, atol=1e-6
+            )
+
+        class ExportStep(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.model = model
+
+            def forward(self, *args):
+                from transformers.cache_utils import DynamicCache
+
+                kv = DynamicCache()
+                for i in range(2):
+                    kv.update(args[2 * i], args[2 * i + 1], i)
+                kwargs = dict(
+                    prefix_pad_masks=args[4], past_key_values=kv, x_t=args[5], timestep=args[6]
+                )
+                if module is pi0 or path == "snapflow_state_out":
+                    kwargs["state"] = args[7]
+                if path.startswith("snapflow"):
+                    kwargs["target_time"] = torch.ones_like(args[6])
+                return self.model.denoise_step(**kwargs)
+
+        class ExportActions(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.model = model
+
+            def forward(self, *args):
+                if path.startswith("snapflow"):
+                    return self.model.sample_actions_1step(*args[:-1], noise=args[-1])
+                return self.model.sample_actions(*args[:-1], noise=args[-1], num_steps=3)
+
+        action_args = (*(sample_obs if path.startswith("snapflow") else obs), noise)
+        exported_actions = torch.export.export(ExportActions(), action_args, strict=False).module()
+        torch.testing.assert_close(
+            exported_actions(*action_args),
+            expected_one_step if path.startswith("snapflow") else expected_actions,
+            rtol=1e-6,
+            atol=1e-6,
+        )
+
+        flat_cache = tuple(v for layer in cache.layers for v in (layer.keys, layer.values))
+        export_args = (*flat_cache, pad, x_t, time, state)
+        exported = torch.export.export(ExportStep(), export_args, strict=False).module()
+        torch.testing.assert_close(exported(*export_args), expected_velocity, rtol=1e-6, atol=1e-6)
+    print(path, "native forward parity passed")
+
+
+if __name__ == "__main__":
+    _check(sys.argv[1])
diff --git a/tests/test_snapflow_backend.py b/tests/test_snapflow_backend.py
index 855baa10..6683e8dc 100644
--- a/tests/test_snapflow_backend.py
+++ b/tests/test_snapflow_backend.py
@@ -284,7 +284,6 @@ def fake_build_prefix(policy, obs):
                 torch.zeros(1, 8, 256), canned_prefix_masks,
                 torch.ones(1, 8, dtype=torch.long),
             )
-            p_mock.model._prepare_attention_masks_4d.return_value = torch.ones(1, 1, 8, 8)
             p_mock.model.paligemma_with_expert.forward.return_value = (None, canned_past_kv)
             # state_proj.weight.dtype is read for the state-dtype cast; set it
             # to match canned_state so the cast is a no-op under mocks.
@@ -347,7 +346,6 @@ def test_student_passes_target_time_through_denoise_step(self):
             torch.ones(1, 8, dtype=torch.bool),
             torch.ones(1, 8, dtype=torch.long),
         )
-        student_mock.model._prepare_attention_masks_4d.return_value = torch.ones(1, 1, 8, 8)
         student_mock.model.paligemma_with_expert.forward.return_value = (None, [("fake_cache",)])
         student_mock.model.paligemma_with_expert.paligemma.model.language_model.config = MagicMock()
         student_mock.model.paligemma_with_expert.gemma_expert.model.config = MagicMock()

From 17a7d0b82796699cd963e4b8de0860c03a20fbe0 Mon Sep 17 00:00:00 2001
From: RomirJ <playindus@gmail.com>
Date: Mon, 12 Oct 2026 15:25:38 -0700
Subject: [PATCH 6/8] Record: LeRobot 0.6.1 on transformers 5.3 is mis-scaled;
 pi0 GPU parity failed

GPU re-run at 5fb92df: pi0 cosine 0.465 / max_abs 0.403 (thresholds 0.999 /
0.1). LeRobot 0.6.1 requires transformers>=5.4; on the pinned 5.3.0 its
native image and language embeddings lose the openpi sqrt(hidden_size)
scale. The exporter now matches that native forward; the receipt harness
still applies the old image shim. Correction appended to the port audit.
Not for merge until the transformers pin decision is made.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../lerobot-06-export-port-20261012.md        | 22 +++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/docs/research/verification/lerobot-06-export-port-20261012.md b/docs/research/verification/lerobot-06-export-port-20261012.md
index 644c39ed..bc3fd0ad 100644
--- a/docs/research/verification/lerobot-06-export-port-20261012.md
+++ b/docs/research/verification/lerobot-06-export-port-20261012.md
@@ -173,3 +173,25 @@ and all edited Python files pass Ruff's undefined-name check. `git diff
   actions max abs 0.118 at line 249). M2 make the pi0.5 patch ignore the prefix pad mask: pi05
   and decomposed fail (velocity max abs 0.012 at line 244). Both mutations killed. VERIFIED.
 - Re-run after review: 48 passed in 77.7 s (`venv-061t`). VERIFIED.
+
+## Correction 2026-10-12 (Claude, after the GPU re-run): native 0.6.1 is mis-scaled on transformers 5.3
+
+The claim above that dropping the `embed_image` sqrt(hidden_size) shim is correct holds only
+for transformers >=5.4. It is **wrong for the pinned transformers 5.3.0**. Evidence:
+
+- LeRobot 0.6.1 declares `transformers>=5.4.0,<5.6.0` (`lerobot-0.6.1.dist-info/METADATA:79`).
+- transformers 5.3.0 `PaliGemmaModel.get_image_features` divides by sqrt(hidden_size)
+  (`modeling_paligemma.py:273`); 5.5.4 does not. LeRobot 0.5.1 multiplied back
+  (`modeling_pi0.py:446`), 0.6.1 does not (`modeling_pi0.py:287-296`).
+- Language embeddings likewise: 0.5.1 `lang_emb * sqrt(dim)` (`modeling_pi0.py:669`); 0.6.1
+  relies on 5.4+'s `GemmaTextScaledWordEmbedding` (5.5.4 `modeling_gemma.py:50-61`), which 5.3.0
+  lacks, and `PiGemmaModel.forward` (`pi_gemma.py:250-290`) has no normalizer.
+- GPU re-run at `5fb92df` (Studio `docs/research/verification/lerobot-06-parity-rerun-20261012.md`):
+  pi0 export vs the receipt harness reference, cosine 0.465, max_abs 0.403, **failed**. The
+  harness (`scripts/local_pi0_monolithic_parity.py:39-56`, `local_pi05_...:48`) still applies the
+  sqrt shim to the reference, the exporter no longer does.
+
+The CPU tests compare against native 0.6.1 under 5.3, which is itself mis-scaled, so they
+passed. The mask port itself stands (review above). Not merged. Fix requires an owner
+decision: move transformers to 5.4/5.5 as LeRobot 0.6.1 requires (re-qualify the exporters), or
+keep 5.3 and restore both openpi scalings for transformers <5.4.

From b7223069a4817f043f0cb15f1ba0d65ef2475763 Mon Sep 17 00:00:00 2001
From: RomirJ <playindus@gmail.com>
Date: Mon, 12 Oct 2026 16:45:17 -0700
Subject: [PATCH 7/8] Move to transformers 5.5.4 (LeRobot 0.6.1's range); add
 pre-migration golden

LeRobot 0.6.1 requires transformers>=5.4,<5.6. On 5.3.0 its pi0/pi0.5 image
and language embeddings lose the openpi sqrt(hidden) scale, which failed GPU
parity at cosine 0.47 while the CPU tests passed against the same mis-scaled
native forward.

- Pins: monolithic/native/eval-local/jetson extras -> transformers==5.5.4,
  base <5.6, [tool.uv] 5.3.0 override removed; exporter gate driven by
  QUALIFIED_TRANSFORMERS_VERSION and refuses 5.3.0.
- Golden reference: tiny pi0/pi0.5 fixtures captured on LeRobot 0.5.1 +
  transformers 5.3.0 + Tether cdb94dc; native, patched export forward and the
  real onnx-diagnostic ONNX export are compared to it (negative control on
  0.6.1 + 5.3.0 fails, prefix norm ratio 1/sqrt(hidden)).
- Receipt harnesses use native LeRobot as the reference (shims removed).
- PaliGemmaBackbone.embed_text_openpi: table lookup * sqrt(hidden), so the
  Triton fast path and Pi0VLA/Pi05VLA do not double-scale on 5.4+.
- Parity vehicles: retries=0, subprocess phase timeouts from measured times
  instead of the SIGKILL watchdog (Modal re-ran the crashed input), restart
  guard, provenance-checked export reuse, new volume root, pi0.5 on A100-40GB.

Full suite: S11 4204 passed; LeRobot 0.6.1 + tf 5.5.4 4262 passed; export
venv subset 73 passed; 0 failures.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 README.md                                     |   2 +-
 .../lerobot-06-export-port-20261012.md        |  63 ++++
 pyproject.toml                                |  35 +--
 scripts/local_pi05_monolithic_parity.py       |  33 +--
 scripts/local_pi0_monolithic_parity.py        |  42 +--
 scripts/modal_groot_export_parity_pinned.py   |   4 +-
 scripts/modal_layer_pruning_profile.py        |   2 +-
 scripts/modal_pi05_export_parity_pinned.py    | 188 +++++++-----
 scripts/modal_pi0_export_parity_pinned.py     | 175 +++++++-----
 scripts/modal_stateout_distill_stage2.py      |   2 +-
 scripts/modal_stateout_smoke.py               |   2 +-
 src/tether/_lerobot_version.py                |   4 +
 src/tether/cli.py                             |   8 +-
 .../diagnostics/check_vlm_tokenization.py     |   6 +-
 src/tether/exporters/decomposed.py            |   2 +-
 src/tether/exporters/monolithic.py            |  15 +-
 src/tether/models/llm/paligemma_backbone.py   |  12 +
 src/tether/models/vlas/pi0.py                 |   4 +-
 src/tether/models/vlas/pi05.py                |   4 +-
 src/tether/runtime/fast_inference/pi05.py     |   5 +-
 tests/_lerobot_pi_golden.py                   | 270 ++++++++++++++++++
 tests/fixtures/lerobot_pi_golden/SHA256SUMS   |   2 +
 tests/fixtures/lerobot_pi_golden/pi0.pt       | Bin 0 -> 327485 bytes
 tests/fixtures/lerobot_pi_golden/pi05.pt      | Bin 0 -> 386805 bytes
 tests/test_cli_export_end_to_end.py           |  18 +-
 tests/test_lerobot_pi_golden.py               |  73 +++++
 tests/test_paligemma_backbone.py              |  22 ++
 tests/test_pi05_vla.py                        |   3 +
 tests/test_pi0_vla.py                         |   3 +
 29 files changed, 727 insertions(+), 272 deletions(-)
 create mode 100644 tests/_lerobot_pi_golden.py
 create mode 100644 tests/fixtures/lerobot_pi_golden/SHA256SUMS
 create mode 100644 tests/fixtures/lerobot_pi_golden/pi0.pt
 create mode 100644 tests/fixtures/lerobot_pi_golden/pi05.pt
 create mode 100644 tests/test_lerobot_pi_golden.py

diff --git a/README.md b/README.md
index ddf88be1..86acd4ce 100644
--- a/README.md
+++ b/README.md
@@ -282,7 +282,7 @@ Advanced/SO-100/internal commands stay callable directly (`config`, `calibrate`,
 
 ### Install notes
 
-- `[monolithic]` extra is required for the cos=+1.000000 verified export path (pins transformers==5.3.0)
+- `[monolithic]` extra is required for the cos=+1.000000 verified export path (pins transformers==5.5.4, the release LeRobot 0.6.1 supports)
 - CPU-only: `pip install 'fastcrest-tether[serve,onnx,monolithic]'`
 - GPU install needs the FULL cuDNN 9 system library (not just the pip wheel). Easiest path: NVIDIA's container `docker run --gpus all -it nvcr.io/nvidia/tensorrt:24.10-py3`, then `apt-get install -y clang` (for lerobot→evdev), then the pip install
 - `tether serve` errors loudly if cuDNN can't load — no silent CPU fallback
diff --git a/docs/research/verification/lerobot-06-export-port-20261012.md b/docs/research/verification/lerobot-06-export-port-20261012.md
index bc3fd0ad..f85642c6 100644
--- a/docs/research/verification/lerobot-06-export-port-20261012.md
+++ b/docs/research/verification/lerobot-06-export-port-20261012.md
@@ -195,3 +195,66 @@ The CPU tests compare against native 0.6.1 under 5.3, which is itself mis-scaled
 passed. The mask port itself stands (review above). Not merged. Fix requires an owner
 decision: move transformers to 5.4/5.5 as LeRobot 0.6.1 requires (re-qualify the exporters), or
 keep 5.3 and restore both openpi scalings for transformers <5.4.
+
+## Resolution 2026-10-12 (Claude): transformers 5.5.4, independent golden reference
+
+Owner decision: move to the transformers release LeRobot 0.6.1 supports. Latest 5.5.x on PyPI
+is 5.5.4 (5.6.x is outside `<5.6.0`). Changes on this branch:
+
+- Pins: `[monolithic]`, `[native]`, `[eval-local]`, `[jetson]` -> `transformers==5.5.4`; base
+  `transformers>=4.40,<5.6`; the `[tool.uv]` 5.3.0 override is removed (LeRobot's own range now
+  holds). `QUALIFIED_TRANSFORMERS_VERSION` (`src/tether/_lerobot_version.py`) drives the exporter
+  gate, which now refuses 5.3.0 by name (`tests/test_cli_export_end_to_end.py`). Every extra
+  resolves with `uv pip compile` for linux x86_64 and darwin arm64 without an override; the
+  dragged pins are tokenizers 0.22.2, huggingface-hub 1.33.0, safetensors 0.8.0, accelerate 1.15.0
+  (within transformers 5.5.4's `tokenizers>=0.22,<=0.23`, `huggingface-hub>=1.5,<2.0`). VERIFIED.
+- Receipt harnesses (`scripts/local_pi0_monolithic_parity.py`, `local_pi05_...`) drop the
+  `embed_image` sqrt shim and the `create_causal_mask` kwarg shim: the reference is native
+  LeRobot 0.6.1 under 5.5.4.
+- Tether's own PaliGemma text embedding (`PaliGemmaBackbone.embed_text_openpi`, used by
+  `models/vlas/pi0.py`, `pi05.py`, `runtime/fast_inference/pi05.py`) now looks the table up
+  directly. 5.5.4's `GemmaTextScaledWordEmbedding` already multiplies by sqrt(hidden)
+  (`modeling_gemma.py:50-61,381-382`), so calling the module and scaling again would have
+  scaled text by `hidden` in the Triton fast path on 5.5.4. VERIFIED by source; regression test
+  `tests/test_paligemma_backbone.py::test_embed_text_openpi_is_independent_of_embedding_module_scaling`.
+- Parity vehicles: `transformers==5.5.4`, `retries=0`, phase subprocess timeouts instead of the
+  SIGKILL watchdog, restart guard, provenance-checked `--reuse-export`, pi0.5 on A100-40GB.
+
+### Independent reference (golden)
+
+`tests/_lerobot_pi_golden.py` + `tests/fixtures/lerobot_pi_golden/{pi0,pi05}.pt`: tiny random
+pi0/pi0.5 (fixed seed, weights stored in the fixture), captured on the qualified OLD stack
+(LeRobot 0.5.1 + transformers 5.3.0 + torch 2.7.1 + Tether `cdb94dc`). The new stack is compared
+to it, not to itself. Results, float32, B=2 with masked cameras and padded language:
+
+| Stack | prefix_embs max_abs | actions (10 steps) max_abs | patched export forward | verdict |
+|---|---|---|---|---|
+| 0.6.1 + 5.5.4 (venv-061) pi0 | 2.98e-08 | 1.79e-07 | 1.79e-07 | pass |
+| 0.6.1 + 5.5.4 pi0.5 | 2.98e-08 | 2.38e-07 | 2.38e-07 | pass |
+| 0.6.1 + 5.3.0 (venv-061t) pi0, negative control | 0.318, norm ratio 0.176777 = 1/sqrt(32) | 0.396 | 0.396 | fail |
+| 0.6.1 + 5.3.0 pi0.5, negative control | same | 0.0175 (cosine 0.99998) | same | fail |
+
+Expected difference: a few float32 ulps, not bit identity. The old stack divides image features
+by sqrt(hidden) inside transformers 5.3 and LeRobot 0.5.1 multiplies back (`modeling_pi0.py:446`);
+5.4+ does neither, and language scaling moved from LeRobot (`modeling_pi0.py:669`, 0.5.1) into
+`GemmaTextScaledWordEmbedding`. Tolerance `rtol=atol=1e-5`. Note the pi0.5 negative control has
+action cosine 0.99998, which a cosine >= 0.999 gate would pass: the prefix-embedding check
+and the absolute tolerance are what catch the bug at tiny width.
+
+The same fixture drives the real monolithic toolchain (`apply_export_patches`,
+`torch_export_patches(patch_transformers=True)`, `torch.export`, `torch.onnx.export` opset 19,
+Where-dtype fix, ONNX Runtime CPU) on torch 2.11.0 + transformers 5.5.4 + onnx-diagnostic 0.9.3:
+pi0 max_abs 1.25e-06, pi0.5 1.19e-07 against the OLD-stack golden at num_steps=10. The ADR-era
+"5.4+ q_length regression" did not reproduce on this toolchain. VERIFIED (CPU, tiny model);
+GPU receipts on the real checkpoints decide.
+
+### Suites
+
+- S11 interpreter, full suite: 4204 passed, 138 skipped, 0 failed.
+- LeRobot 0.6.1 + transformers 5.5.4 + torch 2.7.1 (Studio eval runtime, `venv-061`), full suite:
+  4262 passed, 80 skipped, 0 failed.
+- torch 2.11.0 + transformers 5.5.4 export venv, LeRobot/export subset (golden, patched-forward,
+  CLI export, causal-mask shim, SnapFlow backend, Pad-free mask): 73 passed, 0 failed.
+
+Raw logs: Studio `.sol-scratch/lerobot06/tf55/`; probes and record: Studio
+`docs/research/verification/lerobot-06-transformers55-20261012/`.
diff --git a/pyproject.toml b/pyproject.toml
index 948ebc61..15067e64 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,12 +39,11 @@ dependencies = [
     # loading for the auto-detect path. Apr-14 install-path verification caught
     # that omitting these breaks `tether export` even with [serve,gpu].
     "huggingface_hub>=0.20.0",
-    # Base allows 4.40 → 5.3.x. [monolithic] extra pins ==5.3.0 exactly (5.4+ has
-    # a q_length regression that breaks onnx-diagnostic patches). Without the
-    # upper bound, pip would pull 5.4/5.5 here and conflict with [monolithic].
-    # Without raising the upper bound past 5.0, [monolithic] can't install
-    # alongside the base (the v0.2.1 customer-dogfood finding).
-    "transformers>=4.40,<5.4",
+    # Base allows 4.40 → 5.5.x. [monolithic] pins ==5.5.4 exactly, inside
+    # LeRobot 0.6.1's declared >=5.4,<5.6 (on 5.3 its pi0/pi0.5 embeddings are
+    # mis-scaled; Studio ADR 0003 correction 2026-10-12). The upper bound keeps
+    # pip from pulling an unqualified 5.6+ here.
+    "transformers>=4.40,<5.6",
     "onnx>=1.15.0",
     "onnxscript>=0.1.0",
     # TetherClient (Phase 0.5 customer-sdk) — sync + async HTTP client.
@@ -146,7 +145,7 @@ comply = []
 # instead of hard-failing with ResolutionImpossible. Native parity
 # verification should be run on a Python 3.12+ host.
 native = [
-    "transformers==5.3.0",
+    "transformers==5.5.4",
     "lerobot==0.6.1; python_version >= '3.12'",
     "draccus==0.11.6; python_version >= '3.12'",
     "num2words",
@@ -155,9 +154,9 @@ native = [
 ]
 # Monolithic ONNX export — cos=1.0 verified production path. Runs locally
 # via `tether export --monolithic` (the DEFAULT as of v0.2.1). Requires
-# transformers==5.3.0 EXACTLY (5.4+ has a q_length regression in
-# masking_utils.sdpa_mask that breaks onnx-diagnostic patches). Base pin
-# `transformers>=4.40,<5.4` permits this extra to pin-to-exact.
+# transformers==5.5.4 EXACTLY: LeRobot 0.6.1 needs >=5.4,<5.6, and the
+# exporter is qualified on this release (tests/_lerobot_pi_golden.py runs the
+# full onnx-diagnostic export against the pre-migration golden).
 #
 # lerobot 0.6.x requires Python >=3.12. On Jetson (Python 3.10) the
 # monolithic export can't run locally — export on a cloud/desktop host
@@ -166,7 +165,7 @@ native = [
 monolithic = [
     "lerobot==0.6.1; python_version >= '3.12'",
     "draccus==0.11.6; python_version >= '3.12'",
-    "transformers==5.3.0",
+    "transformers==5.5.4",
     # onnx-diagnostic's export patches reference a torch 2.8+ symbol;
     # _require_monolithic_deps refuses older torch with the same floor.
     "torch>=2.8",
@@ -187,11 +186,10 @@ eval = [
     "gymnasium",
     "h5py",
 ]
-# Spell out the SmolVLA dependencies: its upstream transformers-dep extra
-# requires >=5.4, while ADR 0003 keeps the qualified 5.3.0 masking API.
-# This equivalent explicit set also resolves with pip, which ignores tool.uv.
+# Spell out the SmolVLA dependencies (LeRobot's transformers-dep extra,
+# >=5.4,<5.6) at the exact qualified transformers.
 eval-local = [
-    "transformers==5.3.0; python_version >= '3.12' and sys_platform == 'linux'",
+    "transformers==5.5.4; python_version >= '3.12' and sys_platform == 'linux'",
     "num2words>=0.5.14,<0.6; python_version >= '3.12' and sys_platform == 'linux'",
     "accelerate>=1.14,<2; python_version >= '3.12' and sys_platform == 'linux'",
     "lerobot==0.6.1; python_version >= '3.12' and sys_platform == 'linux'",
@@ -300,7 +298,7 @@ jetson = [
     "jsonschema>=4.0",
     "prometheus-client>=0.19.0",
     # monolithic export stack minus lerobot
-    "transformers==5.3.0",
+    "transformers==5.5.4",
     "onnx-diagnostic>=0.9",
     "onnxscript>=0.1",
     "optree",
@@ -389,8 +387,3 @@ markers = [
     "hardware: requires a physical robot wired in; skipped unless RUN_HARDWARE_TESTS=1",
     "receipt: validates a signed or provenance-bound operational receipt",
 ]
-
-[tool.uv]
-# ADR 0003 owner decision: retain the qualified exporter masking API despite
-# LeRobot 0.6.1 transformers-dep declaring >=5.4,<5.6. Install extras with uv.
-override-dependencies = ["transformers==5.3.0"]
diff --git a/scripts/local_pi05_monolithic_parity.py b/scripts/local_pi05_monolithic_parity.py
index b802f513..3a38ca26 100644
--- a/scripts/local_pi05_monolithic_parity.py
+++ b/scripts/local_pi05_monolithic_parity.py
@@ -34,36 +34,6 @@
     sys.modules[_mod] = _stub
 
 
-def _patch_pi05_for_transformers() -> None:
-    from lerobot.policies.pi05 import modeling_pi05
-    from lerobot.policies import pi_gemma
-    from transformers import masking_utils
-
-    def patched_embed_image(self, image):
-        out_dtype = image.dtype
-        if image.dtype != torch.float32:
-            image = image.to(torch.float32)
-        output = self.paligemma.model.get_image_features(image)
-        features = output.pooler_output if hasattr(output, "pooler_output") else output
-        features = features * self.paligemma.config.text_config.hidden_size**0.5
-        if features.dtype != out_dtype:
-            features = features.to(out_dtype)
-        return features
-
-    modeling_pi05.PaliGemmaWithExpertModel.embed_image = patched_embed_image
-
-    original = masking_utils.create_causal_mask
-
-    def causal_mask_shim(*args, **kwargs):
-        if "inputs_embeds" in kwargs and "input_embeds" not in kwargs:
-            kwargs["input_embeds"] = kwargs.pop("inputs_embeds")
-        return original(*args, **kwargs)
-
-    masking_utils.create_causal_mask = causal_mask_shim
-    if hasattr(pi_gemma, "create_causal_mask"):
-        pi_gemma.create_causal_mask = causal_mask_shim
-
-
 def _repo_commit() -> str:
     root = Path(__file__).resolve().parents[1]
     try:
@@ -160,7 +130,8 @@ def main(argv: list[str] | None = None) -> int:
     if args.num_steps <= 0:
         raise ValueError("--num-steps must be positive.")
 
-    _patch_pi05_for_transformers()
+    # Reference is native LeRobot 0.6.1 under the qualified transformers
+    # (5.5.4): no embedding or mask shims (Studio ADR 0003 correction 2026-10-12).
 
     import onnxruntime as ort
     from huggingface_hub import snapshot_download
diff --git a/scripts/local_pi0_monolithic_parity.py b/scripts/local_pi0_monolithic_parity.py
index 871e3f71..052cb3e3 100644
--- a/scripts/local_pi0_monolithic_parity.py
+++ b/scripts/local_pi0_monolithic_parity.py
@@ -35,44 +35,6 @@
     sys.modules[_mod] = _stub
 
 
-def _patch_pi0_for_transformers_457() -> None:
-    from lerobot.policies.pi0 import modeling_pi0
-
-    def patched_embed_image(self, image):
-        out_dtype = image.dtype
-        if image.dtype != torch.float32:
-            image = image.to(torch.float32)
-        image_outputs = self.paligemma.model.get_image_features(image)
-        features = (
-            image_outputs.pooler_output
-            if hasattr(image_outputs, "pooler_output")
-            else image_outputs
-        )
-        features = features * self.paligemma.config.text_config.hidden_size**0.5
-        if features.dtype != out_dtype:
-            features = features.to(out_dtype)
-        return features
-
-    modeling_pi0.PaliGemmaWithExpertModel.embed_image = patched_embed_image
-
-
-def _patch_create_causal_mask_kwarg() -> None:
-    from transformers import masking_utils
-
-    original = masking_utils.create_causal_mask
-
-    def shim(*args, **kwargs):
-        if "inputs_embeds" in kwargs and "input_embeds" not in kwargs:
-            kwargs["input_embeds"] = kwargs.pop("inputs_embeds")
-        return original(*args, **kwargs)
-
-    masking_utils.create_causal_mask = shim
-    from lerobot.policies import pi_gemma
-
-    if hasattr(pi_gemma, "create_causal_mask"):
-        pi_gemma.create_causal_mask = shim
-
-
 def _repo_commit() -> str:
     root = Path(__file__).resolve().parents[1]
     try:
@@ -167,8 +129,8 @@ def main(argv: list[str] | None = None) -> int:
     if args.num_steps <= 0:
         raise ValueError("--num-steps must be positive.")
 
-    _patch_pi0_for_transformers_457()
-    _patch_create_causal_mask_kwarg()
+    # Reference is native LeRobot 0.6.1 under the qualified transformers
+    # (5.5.4): no embedding or mask shims (Studio ADR 0003 correction 2026-10-12).
 
     import onnxruntime as ort
     from huggingface_hub import snapshot_download
diff --git a/scripts/modal_groot_export_parity_pinned.py b/scripts/modal_groot_export_parity_pinned.py
index 1edf1322..2783269c 100644
--- a/scripts/modal_groot_export_parity_pinned.py
+++ b/scripts/modal_groot_export_parity_pinned.py
@@ -67,9 +67,9 @@ def _hf_secret():
         "torch>=2.8,<2.12",
         "safetensors>=0.4.0",
         "huggingface_hub",
-        # transformers==5.3.0 EXACTLY: the monolithic exporter refuses
+        # transformers==5.5.4 EXACTLY: the monolithic exporter refuses
         # anything else.
-        "transformers==5.3.0",
+        "transformers==5.5.4",
         "onnx>=1.16",
         "onnxruntime>=1.20",
         "onnxscript>=0.1",
diff --git a/scripts/modal_layer_pruning_profile.py b/scripts/modal_layer_pruning_profile.py
index f9f45d37..0e6f910e 100644
--- a/scripts/modal_layer_pruning_profile.py
+++ b/scripts/modal_layer_pruning_profile.py
@@ -66,7 +66,7 @@ def _build_bust() -> str:
     .apt_install("git")
     .pip_install(
         "lerobot==0.6.1",
-        "transformers==5.3.0",
+        "transformers==5.5.4",
         "num2words",
         "safetensors>=0.4.0",
         "scipy",
diff --git a/scripts/modal_pi05_export_parity_pinned.py b/scripts/modal_pi05_export_parity_pinned.py
index 527c1e4f..9445ad01 100644
--- a/scripts/modal_pi05_export_parity_pinned.py
+++ b/scripts/modal_pi05_export_parity_pinned.py
@@ -2,7 +2,7 @@
 
 Runs the receipt-grade harness `scripts/local_pi05_monolithic_parity.py` on
 Modal Linux + CUDA, following the proven pi0 pattern (exact 40-hex model
-commit pin, no floating HEAD, plus an in-function self-kill watchdog):
+commit pin, no floating HEAD, bounded GPU time):
 
 - `lerobot/pi05_base` is pinned to PI05_BASE_REVISION, the last revision
   before "Add relative action processor steps (#6)" (2026-06-03) added a
@@ -14,9 +14,8 @@
 - The Tether implementation under test is pinned to TETHER_REVISION (an
   exact 40-hex commit): the container clones the repo and checks out that
   commit, and the harness records `git rev-parse HEAD` from that checkout.
-- A self-kill wall-clock watchdog bounds GPU burn: `timeout=` on the
-  decorator is the platform's promise; a hung download or stuck CUDA call
-  can sit in the container without tripping it.
+- GPU burn is bounded by per-phase subprocess timeouts, the Modal function
+  timeout and retries=0, plus a restart guard (see EXPORT_TIMEOUT_SECONDS).
 
 Usage:
     modal run scripts/modal_pi05_export_parity_pinned.py
@@ -47,7 +46,7 @@
 )
 
 HF_CACHE_PATH = "/root/.cache/huggingface"
-PARITY_OUT_PATH = "/parity_out/lerobot-061-20261011"
+PARITY_OUT_PATH = "/parity_out/lerobot-061-tf554-20261012"
 
 
 def _hf_secret():
@@ -80,9 +79,9 @@ def _hf_secret():
         "accelerate",
         "draccus==0.11.6",
     )
-    # transformers==5.3.0 EXACTLY: the monolithic exporter refuses anything
-    # else (5.4+ has a q_length regression in masking_utils.sdpa_mask).
-    .pip_install("transformers==5.3.0")
+    # transformers==5.5.4 EXACTLY: LeRobot 0.6.1 needs >=5.4,<5.6 and the
+    # monolithic exporter gate refuses anything but the qualified release.
+    .pip_install("transformers==5.5.4")
     .env({
         "HF_HOME": HF_CACHE_PATH,
         "TRANSFORMERS_CACHE": f"{HF_CACHE_PATH}/transformers",
@@ -118,10 +117,27 @@ def _hf_secret():
 )
 
 
+# Phase budgets from measured runs: pi0 fresh export 1776.5 s and harness
+# incl. setup 274.9 s on A100-40GB (Studio
+# docs/research/verification/lerobot-06-parity-rerun-20261012.md); pi0.5
+# fresh export plus failed session about 2190 s on A10G
+# (docs/parity/parity-pi05-refix-report.md). About 1.5x and 3x margin. Each phase runs in a subprocess with this timeout, so an
+# overrun ends the input as an ordinary failure. The previous in-container
+# SIGKILL watchdog surfaced as a runner crash, which Modal treats as an
+# internal failure and re-schedules regardless of the retry policy.
+EXPORT_TIMEOUT_SECONDS = 2700
+PARITY_TIMEOUT_SECONDS = 900
+SETUP_MARGIN_SECONDS = 600
+MODAL_TIMEOUT_SECONDS = EXPORT_TIMEOUT_SECONDS + PARITY_TIMEOUT_SECONDS + SETUP_MARGIN_SECONDS
+
+
 @app.function(
     image=gpu_image,
-    gpu="A10G",
-    timeout=3600,
+    # A100-40GB: on A10G the fp32 reference and the ~13 GB ORT session do
+    # not co-reside (docs/parity/parity-pi05-refix-report.md, run 1 OOM).
+    gpu="A100-40GB",
+    timeout=MODAL_TIMEOUT_SECONDS,
+    retries=0,
     volumes={
         HF_CACHE_PATH: _hf_cache_volume,
         "/parity_out": _parity_out_volume,
@@ -133,39 +149,24 @@ def run_export_parity(
     tether_revision: str = TETHER_REVISION,
     num_steps: int = 10,
     reuse_export: bool = False,
-    watchdog_seconds: float = 3300,
+    export_timeout_seconds: float = EXPORT_TIMEOUT_SECONDS,
+    parity_timeout_seconds: float = PARITY_TIMEOUT_SECONDS,
+    run_id: str = "",
 ) -> dict:
     import subprocess
     import sys
     import time
     from pathlib import Path
 
+    import json as _json
     import os as _os
-    import signal as _signal
-    import threading as _threading
-
-    _WATCHDOG_SECONDS = float(
-        _os.environ.get("PARITY_WATCHDOG_SECONDS", str(watchdog_seconds))
-    )
-
-    def _self_kill() -> None:
-        print(
-            f"[watchdog] deadline of {_WATCHDOG_SECONDS:.0f}s reached; killing this "
-            "container rather than burning GPU time on a hung step.",
-            flush=True,
-        )
-        _os.kill(_os.getpid(), _signal.SIGKILL)
-
-    _watchdog = _threading.Timer(_WATCHDOG_SECONDS, _self_kill)
-    _watchdog.daemon = True
-    _watchdog.start()
 
     _started_at = time.monotonic()
 
     def _progress(stage: str) -> None:
         print(f"[progress] {time.monotonic() - _started_at:7.1f}s  {stage}", flush=True)
 
-    _progress("container up, watchdog armed")
+    _progress("container up")
     summary: dict = {
         "model_source": "lerobot/pi05_base",
         "model_revision": model_revision,
@@ -174,6 +175,20 @@ def _progress(stage: str) -> None:
         "num_steps": num_steps,
     }
 
+    # Restart guard: if Modal re-schedules this input after a container
+    # crash, the second attempt finds its own marker and exits at once
+    # instead of starting another GPU export.
+    if run_id:
+        marker = Path(PARITY_OUT_PATH) / "pi05" / "attempts" / run_id
+        _parity_out_volume.reload()
+        if marker.exists():
+            _progress("restarted attempt detected; refusing to run again")
+            summary["verdict"] = "aborted-restarted-attempt"
+            return summary
+        marker.parent.mkdir(parents=True, exist_ok=True)
+        marker.write_text(str(time.time()))
+        _parity_out_volume.commit()
+
     # Fail fast if the CUDA EP cannot be created (a broken CUDA/cuDNN
     # image otherwise surfaces 10 minutes later as a no-fallback gate
     # failure after the reference has already run).
@@ -241,46 +256,58 @@ def _progress(stage: str) -> None:
     snap = snapshot_download("lerobot/pi05_base", revision=model_revision)
     summary["snapshot_dir"] = snap
 
-    from tether.exporters.monolithic import export_pi05_monolithic
-
     export_dir = Path(PARITY_OUT_PATH) / "pi05" / "export"
-    existing = export_dir / "model.onnx"
-    # model.onnx may be graph-only with weights in model.onnx.data: count
-    # the whole artifact dir, which is also what the receipt hashes.
-    existing_bytes = (
-        sum(f.stat().st_size for f in export_dir.glob("*") if f.is_file())
-        if existing.is_file()
-        else 0
+    provenance_path = export_dir / "tether-export-provenance.json"
+    harness_env = dict(_os.environ)
+    harness_env["PYTHONPATH"] = str(pin_dir / "src") + _os.pathsep + harness_env.get(
+        "PYTHONPATH", ""
     )
-    if reuse_export and existing_bytes > 10**9:
-        export_result = {
-            "status": "reused",
-            "onnx_path": str(existing),
-            "size_mb": existing_bytes / 1e6,
-            "num_steps": num_steps,
-        }
-        _progress(f"reusing retained export ({export_result['size_mb']:.1f}MB)")
+    from importlib.metadata import version as _v
+
+    provenance = {
+        "tether_revision": tether_revision,
+        "model_revision": model_revision,
+        "num_steps": num_steps,
+        **{p: _v(p) for p in ("lerobot", "transformers", "torch", "onnx-diagnostic", "onnxscript")},
+    }
+    summary["export_provenance"] = provenance
+    retained = (
+        _json.loads(provenance_path.read_text()) if provenance_path.is_file() else None
+    )
+    if reuse_export and retained == provenance:
+        export_result = {"status": "reused", "onnx_path": str(export_dir / "model.onnx")}
+        _progress("reusing retained export (provenance matches)")
     else:
-        _progress("export_pi05_monolithic from pinned snapshot")
-        export_result = export_pi05_monolithic(snap, export_dir, num_steps=num_steps)
+        if reuse_export:
+            _progress(f"not reusing: retained provenance {retained} != {provenance}")
+        _progress("export_pi05_monolithic from pinned snapshot (subprocess)")
+        code = (
+            "import json, sys; from tether.exporters.monolithic import export_pi05_monolithic; "
+            "r = export_pi05_monolithic(sys.argv[1], sys.argv[2], num_steps=int(sys.argv[3])); "
+            "print('EXPORT_RESULT ' + json.dumps(r), flush=True)"
+        )
+        try:
+            proc = subprocess.run(
+                [sys.executable, "-c", code, snap, str(export_dir), str(num_steps)],
+                cwd=str(pin_dir), env=harness_env, capture_output=True, text=True,
+                timeout=export_timeout_seconds,
+            )
+        except subprocess.TimeoutExpired as exc:
+            _parity_out_volume.commit()
+            raise RuntimeError(
+                f"export exceeded {export_timeout_seconds:.0f}s; stopped"
+            ) from exc
+        print(proc.stdout[-4000:], flush=True)
+        print(proc.stderr[-4000:], flush=True)
+        if proc.returncode != 0:
+            raise RuntimeError(f"export failed with return code {proc.returncode}")
+        line = next(x for x in proc.stdout.splitlines() if x.startswith("EXPORT_RESULT "))
+        export_result = _json.loads(line.split(" ", 1)[1])
+        provenance_path.write_text(_json.dumps(provenance, sort_keys=True) + "\n")
+        _parity_out_volume.commit()
         _progress(f"export ok: {export_result.get('size_mb', 0):.1f}MB")
     summary["export"] = export_result
 
-    # Release the export phase's cached GPU blocks before the parity
-    # subprocess: the harness places the reference on CUDA, and the
-    # parent's caching-allocator reservation would otherwise OOM it
-    # (observed: parent held 22.03/22.06 GiB, child got 16 MiB free).
-    import gc as _gc
-
-    _gc.collect()
-    try:
-        import torch as _torch
-
-        if _torch.cuda.is_available():
-            _torch.cuda.empty_cache()
-    except Exception as exc:  # noqa: BLE001 -- diagnostic only
-        print(f"[warn] cuda cache release failed: {exc!r}", flush=True)
-    _progress("parent GPU cache released")
 
     # 3. Receipt-grade parity with CUDA, CPU fallback disabled.
     receipt_path = Path(PARITY_OUT_PATH) / "pi05" / "pi05-export-parity-receipt.json"
@@ -295,19 +322,18 @@ def _progress(stage: str) -> None:
         "--receipt", str(receipt_path),
     ]
     _progress("running receipt-grade parity (CUDA, no CPU fallback)")
-    harness_env = dict(_os.environ)
-    harness_env["PYTHONPATH"] = str(pin_dir / "src") + _os.pathsep + harness_env.get(
-        "PYTHONPATH", ""
-    )
-    proc = subprocess.run(
-        cmd, cwd=str(pin_dir), capture_output=True, text=True, env=harness_env
-    )
+    try:
+        proc = subprocess.run(
+            cmd, cwd=str(pin_dir), capture_output=True, text=True, env=harness_env,
+            timeout=parity_timeout_seconds,
+        )
+    except subprocess.TimeoutExpired as exc:
+        _parity_out_volume.commit()
+        raise RuntimeError(f"parity harness exceeded {parity_timeout_seconds:.0f}s; stopped") from exc
     print(proc.stdout[-6000:], flush=True)
     print(proc.stderr[-3000:], flush=True)
     summary["harness_returncode"] = proc.returncode
 
-    import json as _json
-
     if receipt_path.is_file():
         receipt = _json.loads(receipt_path.read_text())
         summary["verdict"] = receipt.get("verdict")
@@ -323,7 +349,6 @@ def _progress(stage: str) -> None:
         summary["verdict"] = "no-receipt"
 
     _parity_out_volume.commit()
-    _watchdog.cancel()
     _progress("done")
     return summary
 
@@ -331,10 +356,21 @@ def _progress(stage: str) -> None:
 @app.local_entrypoint()
 def main(
     tether_revision: str = TETHER_REVISION,
-    watchdog_seconds: float = 1800,
+    reuse_export: bool = False,
+    num_steps: int = 10,
+    export_timeout_seconds: float = EXPORT_TIMEOUT_SECONDS,
+    parity_timeout_seconds: float = PARITY_TIMEOUT_SECONDS,
+    run_id: str = "",
 ) -> None:
+    import uuid
+
     result = run_export_parity.remote(
-        tether_revision=tether_revision, watchdog_seconds=watchdog_seconds
+        tether_revision=tether_revision,
+        reuse_export=reuse_export,
+        num_steps=num_steps,
+        export_timeout_seconds=export_timeout_seconds,
+        parity_timeout_seconds=parity_timeout_seconds,
+        run_id=run_id or uuid.uuid4().hex,
     )
     print("\n=== RESULT ===")
     for key, value in result.items():
diff --git a/scripts/modal_pi0_export_parity_pinned.py b/scripts/modal_pi0_export_parity_pinned.py
index 61ec89c5..6b9f9883 100644
--- a/scripts/modal_pi0_export_parity_pinned.py
+++ b/scripts/modal_pi0_export_parity_pinned.py
@@ -11,7 +11,8 @@
 - A100-40GB: the fp32 reference + ~13 GB session cannot co-reside on A10G
   (pi0.5 refix OOM'd allocating a 128 MB arena there); this is a tier
   change only, gates unmodified.
-- In-function self-kill watchdog bounds GPU burn.
+- GPU burn is bounded by per-phase subprocess timeouts, the Modal function
+  timeout and retries=0, plus a restart guard (see EXPORT_TIMEOUT_SECONDS).
 
 Usage:
     modal run scripts/modal_pi0_export_parity_pinned.py
@@ -39,7 +40,7 @@
 )
 
 HF_CACHE_PATH = "/root/.cache/huggingface"
-PARITY_OUT_PATH = "/parity_out/lerobot-061-20261011"
+PARITY_OUT_PATH = "/parity_out/lerobot-061-tf554-20261012"
 
 
 def _hf_secret():
@@ -72,9 +73,9 @@ def _hf_secret():
         "accelerate",
         "draccus==0.11.6",
     )
-    # transformers==5.3.0 EXACTLY: the monolithic exporter refuses anything
-    # else (5.4+ has a q_length regression in masking_utils.sdpa_mask).
-    .pip_install("transformers==5.3.0")
+    # transformers==5.5.4 EXACTLY: LeRobot 0.6.1 needs >=5.4,<5.6 and the
+    # monolithic exporter gate refuses anything but the qualified release.
+    .pip_install("transformers==5.5.4")
     .env({
         "HF_HOME": HF_CACHE_PATH,
         "TRANSFORMERS_CACHE": f"{HF_CACHE_PATH}/transformers",
@@ -110,10 +111,25 @@ def _hf_secret():
 )
 
 
+# Phase budgets from measured runs: pi0 fresh export 1776.5 s and harness
+# incl. setup 274.9 s on A100-40GB (Studio
+# docs/research/verification/lerobot-06-parity-rerun-20261012.md); pi0.5
+# fresh export plus failed session about 2190 s on A10G
+# (docs/parity/parity-pi05-refix-report.md). About 1.5x and 3x margin. Each phase runs in a subprocess with this timeout, so an
+# overrun ends the input as an ordinary failure. The previous in-container
+# SIGKILL watchdog surfaced as a runner crash, which Modal treats as an
+# internal failure and re-schedules regardless of the retry policy.
+EXPORT_TIMEOUT_SECONDS = 2700
+PARITY_TIMEOUT_SECONDS = 900
+SETUP_MARGIN_SECONDS = 600
+MODAL_TIMEOUT_SECONDS = EXPORT_TIMEOUT_SECONDS + PARITY_TIMEOUT_SECONDS + SETUP_MARGIN_SECONDS
+
+
 @app.function(
     image=gpu_image,
     gpu="A100-40GB",
-    timeout=3600,
+    timeout=MODAL_TIMEOUT_SECONDS,
+    retries=0,
     volumes={
         HF_CACHE_PATH: _hf_cache_volume,
         "/parity_out": _parity_out_volume,
@@ -125,39 +141,24 @@ def run_export_parity(
     tether_revision: str = TETHER_REVISION,
     num_steps: int = 10,
     reuse_export: bool = False,
-    watchdog_seconds: float = 3300,
+    export_timeout_seconds: float = EXPORT_TIMEOUT_SECONDS,
+    parity_timeout_seconds: float = PARITY_TIMEOUT_SECONDS,
+    run_id: str = "",
 ) -> dict:
     import subprocess
     import sys
     import time
     from pathlib import Path
 
+    import json as _json
     import os as _os
-    import signal as _signal
-    import threading as _threading
-
-    _WATCHDOG_SECONDS = float(
-        _os.environ.get("PARITY_WATCHDOG_SECONDS", str(watchdog_seconds))
-    )
-
-    def _self_kill() -> None:
-        print(
-            f"[watchdog] deadline of {_WATCHDOG_SECONDS:.0f}s reached; killing this "
-            "container rather than burning GPU time on a hung step.",
-            flush=True,
-        )
-        _os.kill(_os.getpid(), _signal.SIGKILL)
-
-    _watchdog = _threading.Timer(_WATCHDOG_SECONDS, _self_kill)
-    _watchdog.daemon = True
-    _watchdog.start()
 
     _started_at = time.monotonic()
 
     def _progress(stage: str) -> None:
         print(f"[progress] {time.monotonic() - _started_at:7.1f}s  {stage}", flush=True)
 
-    _progress("container up, watchdog armed")
+    _progress("container up")
     summary: dict = {
         "model_source": "lerobot/pi0_base",
         "model_revision": model_revision,
@@ -166,6 +167,20 @@ def _progress(stage: str) -> None:
         "num_steps": num_steps,
     }
 
+    # Restart guard: if Modal re-schedules this input after a container
+    # crash, the second attempt finds its own marker and exits at once
+    # instead of starting another GPU export.
+    if run_id:
+        marker = Path(PARITY_OUT_PATH) / "pi0" / "attempts" / run_id
+        _parity_out_volume.reload()
+        if marker.exists():
+            _progress("restarted attempt detected; refusing to run again")
+            summary["verdict"] = "aborted-restarted-attempt"
+            return summary
+        marker.parent.mkdir(parents=True, exist_ok=True)
+        marker.write_text(str(time.time()))
+        _parity_out_volume.commit()
+
     # Fail fast if the CUDA EP cannot be created (a broken CUDA/cuDNN
     # image otherwise surfaces much later as a no-fallback gate failure
     # after the reference has already run).
@@ -233,46 +248,58 @@ def _progress(stage: str) -> None:
     snap = snapshot_download("lerobot/pi0_base", revision=model_revision)
     summary["snapshot_dir"] = snap
 
-    from tether.exporters.monolithic import export_pi0_monolithic
-
     export_dir = Path(PARITY_OUT_PATH) / "pi0" / "export"
-    existing = export_dir / "model.onnx"
-    # model.onnx may be graph-only with weights in model.onnx.data: count
-    # the whole artifact dir, which is also what the receipt hashes.
-    existing_bytes = (
-        sum(f.stat().st_size for f in export_dir.glob("*") if f.is_file())
-        if existing.is_file()
-        else 0
+    provenance_path = export_dir / "tether-export-provenance.json"
+    harness_env = dict(_os.environ)
+    harness_env["PYTHONPATH"] = str(pin_dir / "src") + _os.pathsep + harness_env.get(
+        "PYTHONPATH", ""
     )
-    if reuse_export and existing_bytes > 10**9:
-        export_result = {
-            "status": "reused",
-            "onnx_path": str(existing),
-            "size_mb": existing_bytes / 1e6,
-            "num_steps": num_steps,
-        }
-        _progress(f"reusing retained export ({export_result['size_mb']:.1f}MB)")
+    from importlib.metadata import version as _v
+
+    provenance = {
+        "tether_revision": tether_revision,
+        "model_revision": model_revision,
+        "num_steps": num_steps,
+        **{p: _v(p) for p in ("lerobot", "transformers", "torch", "onnx-diagnostic", "onnxscript")},
+    }
+    summary["export_provenance"] = provenance
+    retained = (
+        _json.loads(provenance_path.read_text()) if provenance_path.is_file() else None
+    )
+    if reuse_export and retained == provenance:
+        export_result = {"status": "reused", "onnx_path": str(export_dir / "model.onnx")}
+        _progress("reusing retained export (provenance matches)")
     else:
-        _progress("export_pi0_monolithic from pinned snapshot")
-        export_result = export_pi0_monolithic(snap, export_dir, num_steps=num_steps)
+        if reuse_export:
+            _progress(f"not reusing: retained provenance {retained} != {provenance}")
+        _progress("export_pi0_monolithic from pinned snapshot (subprocess)")
+        code = (
+            "import json, sys; from tether.exporters.monolithic import export_pi0_monolithic; "
+            "r = export_pi0_monolithic(sys.argv[1], sys.argv[2], num_steps=int(sys.argv[3])); "
+            "print('EXPORT_RESULT ' + json.dumps(r), flush=True)"
+        )
+        try:
+            proc = subprocess.run(
+                [sys.executable, "-c", code, snap, str(export_dir), str(num_steps)],
+                cwd=str(pin_dir), env=harness_env, capture_output=True, text=True,
+                timeout=export_timeout_seconds,
+            )
+        except subprocess.TimeoutExpired as exc:
+            _parity_out_volume.commit()
+            raise RuntimeError(
+                f"export exceeded {export_timeout_seconds:.0f}s; stopped"
+            ) from exc
+        print(proc.stdout[-4000:], flush=True)
+        print(proc.stderr[-4000:], flush=True)
+        if proc.returncode != 0:
+            raise RuntimeError(f"export failed with return code {proc.returncode}")
+        line = next(x for x in proc.stdout.splitlines() if x.startswith("EXPORT_RESULT "))
+        export_result = _json.loads(line.split(" ", 1)[1])
+        provenance_path.write_text(_json.dumps(provenance, sort_keys=True) + "\n")
+        _parity_out_volume.commit()
         _progress(f"export ok: {export_result.get('size_mb', 0):.1f}MB")
     summary["export"] = export_result
 
-    # Release the export phase's cached GPU blocks before the parity
-    # subprocess: the harness places the reference on CUDA, and the
-    # parent's caching-allocator reservation would otherwise OOM it
-    # (observed on pi0.5: parent held 22.03/22.06 GiB, child got 16 MiB).
-    import gc as _gc
-
-    _gc.collect()
-    try:
-        import torch as _torch
-
-        if _torch.cuda.is_available():
-            _torch.cuda.empty_cache()
-    except Exception as exc:  # noqa: BLE001 -- diagnostic only
-        print(f"[warn] cuda cache release failed: {exc!r}", flush=True)
-    _progress("parent GPU cache released")
 
     # 3. Receipt-grade parity with CUDA, CPU fallback disabled.
     receipt_path = Path(PARITY_OUT_PATH) / "pi0" / "pi0-export-parity-receipt.json"
@@ -287,19 +314,18 @@ def _progress(stage: str) -> None:
         "--receipt", str(receipt_path),
     ]
     _progress("running receipt-grade parity (CUDA, no CPU fallback)")
-    harness_env = dict(_os.environ)
-    harness_env["PYTHONPATH"] = str(pin_dir / "src") + _os.pathsep + harness_env.get(
-        "PYTHONPATH", ""
-    )
-    proc = subprocess.run(
-        cmd, cwd=str(pin_dir), capture_output=True, text=True, env=harness_env
-    )
+    try:
+        proc = subprocess.run(
+            cmd, cwd=str(pin_dir), capture_output=True, text=True, env=harness_env,
+            timeout=parity_timeout_seconds,
+        )
+    except subprocess.TimeoutExpired as exc:
+        _parity_out_volume.commit()
+        raise RuntimeError(f"parity harness exceeded {parity_timeout_seconds:.0f}s; stopped") from exc
     print(proc.stdout[-6000:], flush=True)
     print(proc.stderr[-3000:], flush=True)
     summary["harness_returncode"] = proc.returncode
 
-    import json as _json
-
     if receipt_path.is_file():
         receipt = _json.loads(receipt_path.read_text())
         summary["verdict"] = receipt.get("verdict")
@@ -315,7 +341,6 @@ def _progress(stage: str) -> None:
         summary["verdict"] = "no-receipt"
 
     _parity_out_volume.commit()
-    _watchdog.cancel()
     _progress("done")
     return summary
 
@@ -325,13 +350,19 @@ def main(
     tether_revision: str = TETHER_REVISION,
     reuse_export: bool = False,
     num_steps: int = 10,
-    watchdog_seconds: float = 1800,
+    export_timeout_seconds: float = EXPORT_TIMEOUT_SECONDS,
+    parity_timeout_seconds: float = PARITY_TIMEOUT_SECONDS,
+    run_id: str = "",
 ) -> None:
+    import uuid
+
     result = run_export_parity.remote(
         tether_revision=tether_revision,
         reuse_export=reuse_export,
         num_steps=num_steps,
-        watchdog_seconds=watchdog_seconds,
+        export_timeout_seconds=export_timeout_seconds,
+        parity_timeout_seconds=parity_timeout_seconds,
+        run_id=run_id or uuid.uuid4().hex,
     )
     print("\n=== RESULT ===")
     for key, value in result.items():
diff --git a/scripts/modal_stateout_distill_stage2.py b/scripts/modal_stateout_distill_stage2.py
index f2e3d357..ee02bcb6 100644
--- a/scripts/modal_stateout_distill_stage2.py
+++ b/scripts/modal_stateout_distill_stage2.py
@@ -69,7 +69,7 @@ def _bust():
     .apt_install("git", "ffmpeg")
     .pip_install(
         "lerobot==0.6.1",
-        "transformers==5.3.0",
+        "transformers==5.5.4",
         "num2words",
         "safetensors>=0.4.0",
         "numpy",
diff --git a/scripts/modal_stateout_smoke.py b/scripts/modal_stateout_smoke.py
index 6b1307cf..b0281a5e 100644
--- a/scripts/modal_stateout_smoke.py
+++ b/scripts/modal_stateout_smoke.py
@@ -59,7 +59,7 @@ def _bust():
     .apt_install("git")
     .pip_install(
         "lerobot==0.6.1",
-        "transformers==5.3.0",
+        "transformers==5.5.4",
         "num2words",
         "safetensors>=0.4.0",
         "numpy",
diff --git a/src/tether/_lerobot_version.py b/src/tether/_lerobot_version.py
index a88eefb8..c1d0d01a 100644
--- a/src/tether/_lerobot_version.py
+++ b/src/tether/_lerobot_version.py
@@ -1,3 +1,7 @@
 """LeRobot runtime qualified for dataset admission and export (ADR 0003)."""
 
 QUALIFIED_LEROBOT_VERSION = "0.6.1"
+# LeRobot 0.6.1 declares transformers>=5.4,<5.6 and its pi0/pi0.5 code relies
+# on 5.4+ embedding scaling; on 5.3 the image and language embeddings are
+# 1/sqrt(hidden) too small (Studio ADR 0003, correction 2026-10-12).
+QUALIFIED_TRANSFORMERS_VERSION = "5.5.4"
diff --git a/src/tether/cli.py b/src/tether/cli.py
index 1154889d..249bd5aa 100644
--- a/src/tether/cli.py
+++ b/src/tether/cli.py
@@ -237,7 +237,7 @@ def export(
              "path, one ONNX file). Opt into --decomposed only if you specifically need "
              "the 5-stage export for debugging; --decomposed is the older path with "
              "known correctness gaps. Monolithic requires `pip install "
-             "'fastcrest-tether[monolithic]'` (pins transformers==5.3.0).",
+             "'fastcrest-tether[monolithic]'` (pins transformers==5.5.4).",
     ),
     export_mode: str = typer.Option(
         "auto",
@@ -363,7 +363,7 @@ def export(
                 err_console.print(f"[red]{exc}[/red]", markup=False)
                 err_console.print(
                     "\nFix: pip install 'fastcrest-tether[monolithic]' "
-                    "(pins transformers==5.3.0; use a clean venv to avoid "
+                    "(pins transformers==5.5.4; use a clean venv to avoid "
                     "the base transformers<5.0 conflict)",
                     style="cyan", markup=False,
                 )
@@ -459,7 +459,7 @@ def export(
                 err_console.print(f"[red]{exc}[/red]", markup=False)
                 err_console.print(
                     "\nFix: pip install 'fastcrest-tether[monolithic]' "
-                    "(pins transformers==5.3.0; use a clean venv to avoid "
+                    "(pins transformers==5.5.4; use a clean venv to avoid "
                     "the base transformers<5.0 conflict)",
                     style="cyan", markup=False,
                 )
@@ -5606,7 +5606,7 @@ def go(
                 console.print(
                     "\n`tether go` needs the monolithic export extras to deploy this model.\n"
                     "Fix: pip install 'fastcrest-tether[monolithic]'\n"
-                    "(pins transformers==5.3.0; use a clean venv to avoid the "
+                    "(pins transformers==5.5.4; use a clean venv to avoid the "
                     "base transformers<5.0 conflict)",
                     style="cyan", markup=False,
                 )
diff --git a/src/tether/diagnostics/check_vlm_tokenization.py b/src/tether/diagnostics/check_vlm_tokenization.py
index a005442e..386c789b 100644
--- a/src/tether/diagnostics/check_vlm_tokenization.py
+++ b/src/tether/diagnostics/check_vlm_tokenization.py
@@ -123,8 +123,8 @@ def _run(model_path: str, **kwargs) -> CheckResult:
             actual=f"AutoTokenizer raised {type(e).__name__}: {e}",
             remediation=(
                 f"Export has tokenizer files but they are malformed. Check the "
-                f"export step's logs OR pin transformers==5.3.0 per ADR "
-                f"2026-04-17 onnx-export-gotchas. See {GH_ISSUE}."
+                f"export step's logs OR install the qualified transformers "
+                f"(pip install 'fastcrest-tether[monolithic]'). See {GH_ISSUE}."
             ),
             duration_ms=0.0,
             github_issue=GH_ISSUE,
@@ -150,7 +150,7 @@ def _run(model_path: str, **kwargs) -> CheckResult:
             remediation=(
                 f"Tokenizer/model vocab_size mismatch — tokens > {vocab_size} will "
                 f"crash the ONNX path. Likely cause: transformers version drift. "
-                f"Pin transformers==5.3.0 per ADR 2026-04-17."
+                f"Install the qualified transformers: pip install 'fastcrest-tether[monolithic]'."
             ),
             duration_ms=0.0,
             github_issue=GH_ISSUE,
diff --git a/src/tether/exporters/decomposed.py b/src/tether/exporters/decomposed.py
index 1d9a8ba3..5852d6bf 100644
--- a/src/tether/exporters/decomposed.py
+++ b/src/tether/exporters/decomposed.py
@@ -38,7 +38,7 @@
 
 ## Dependencies
 
-Same as ``monolithic`` extra: transformers==5.3.0, lerobot==0.6.1,
+Same as ``monolithic`` extra: transformers==5.5.4, lerobot==0.6.1,
 onnx-diagnostic>=0.9. ``apply_export_patches`` from ``monolithic.py``
 is invoked here to get the shared denoise-step + cache-freeze patches.
 
diff --git a/src/tether/exporters/monolithic.py b/src/tether/exporters/monolithic.py
index 9a832d52..f5e00e58 100644
--- a/src/tether/exporters/monolithic.py
+++ b/src/tether/exporters/monolithic.py
@@ -11,7 +11,7 @@
 
 Requires (pinned in the ``monolithic`` extra):
     lerobot==0.6.1
-    transformers==5.3.0   (5.4+ has a q_length scalar regression)
+    transformers==5.5.4   (LeRobot 0.6.1 requires >=5.4,<5.6)
     onnx-diagnostic>=0.9
     onnxscript>=0.1
     optree, scipy, num2words
@@ -23,7 +23,7 @@
 """
 from __future__ import annotations
 
-from tether._lerobot_version import QUALIFIED_LEROBOT_VERSION
+from tether._lerobot_version import QUALIFIED_LEROBOT_VERSION, QUALIFIED_TRANSFORMERS_VERSION
 
 import logging
 import time
@@ -160,15 +160,16 @@ def _require_monolithic_deps() -> None:
     missing = []
     try:
         import transformers
-        if transformers.__version__ != "5.3.0":
+        if transformers.__version__ != QUALIFIED_TRANSFORMERS_VERSION:
             raise ImportError(
                 f"transformers {transformers.__version__} detected; the "
-                f"monolithic export requires exactly 5.3.0 (5.4+ has a "
-                f"q_length regression in masking_utils.sdpa_mask). Install "
-                f"with: pip install transformers==5.3.0"
+                f"monolithic export requires exactly {QUALIFIED_TRANSFORMERS_VERSION} "
+                f"(LeRobot {QUALIFIED_LEROBOT_VERSION} needs >=5.4: on 5.3 its "
+                f"pi0/pi0.5 embeddings are mis-scaled). Install with: "
+                f"pip install transformers=={QUALIFIED_TRANSFORMERS_VERSION}"
             )
     except ImportError as e:
-        missing.append(f"transformers==5.3.0 ({e})")
+        missing.append(f"transformers=={QUALIFIED_TRANSFORMERS_VERSION} ({e})")
 
     # onnx-diagnostic 0.8.11 and 0.9.0-0.9.3 unconditionally patch
     # torch._export.non_strict_utils._constrain_user_specified_dimhint_range,
diff --git a/src/tether/models/llm/paligemma_backbone.py b/src/tether/models/llm/paligemma_backbone.py
index 6c53b54d..f794e7c9 100644
--- a/src/tether/models/llm/paligemma_backbone.py
+++ b/src/tether/models/llm/paligemma_backbone.py
@@ -110,6 +110,18 @@ def embed_tokens(self) -> nn.Module:
         before merging in the projected image embeds."""
         return self.language_model.embed_tokens
 
+    def embed_text_openpi(self, token_ids: torch.Tensor) -> torch.Tensor:
+        """openpi text embedding: raw table lookup times sqrt(hidden).
+
+        Looks up the weight directly because the module's own scaling moved
+        between transformers releases: 5.3 Gemma uses a plain nn.Embedding
+        (scaled later in GemmaModel.forward), 5.4+ uses
+        GemmaTextScaledWordEmbedding, which already multiplies by sqrt(hidden).
+        Calling the module and scaling again double-scales on 5.4+.
+        """
+        weight = self.embed_tokens.weight
+        return torch.nn.functional.embedding(token_ids, weight) * self.text_hidden_size**0.5
+
     @property
     def text_hidden_size(self) -> int:
         """Hidden dim of the language tower (1152 for PaliGemma-3B-pt-224)."""
diff --git a/src/tether/models/vlas/pi0.py b/src/tether/models/vlas/pi0.py
index 932505d5..fcf184eb 100644
--- a/src/tether/models/vlas/pi0.py
+++ b/src/tether/models/vlas/pi0.py
@@ -398,8 +398,6 @@ def predict_action(
         # protocol: pre-scale text externally; image is raw multi_modal_projector
         # output (no additional scaling needed because the get_image_features
         # path isn't used — we call multi_modal_projector directly).
-        text_hidden = self.llm_backbone.text_hidden_size
-        sqrt_h = text_hidden ** 0.5
         image_embeds_list: list[torch.Tensor] = []
         for img in images:
             # SigLIP: [B, 3, 224, 224] → [B, 256, vision_hidden=1152]
@@ -410,7 +408,7 @@ def predict_action(
             image_embeds_list.append(img_emb)
 
         # Pre-scale text per lerobot embed_prefix at modeling_pi0.py:669.
-        text_embs = self.llm_backbone.embed_tokens(lang_tokens) * sqrt_h
+        text_embs = self.llm_backbone.embed_text_openpi(lang_tokens)
 
         # ─── 4. Concat into prefix ──────────────────────────────────────
         prefix_embs = torch.cat([*image_embeds_list, text_embs], dim=1)
diff --git a/src/tether/models/vlas/pi05.py b/src/tether/models/vlas/pi05.py
index 50302853..bc108511 100644
--- a/src/tether/models/vlas/pi05.py
+++ b/src/tether/models/vlas/pi05.py
@@ -304,14 +304,12 @@ def predict_action(
         # Same scaling protocol as Pi0VLA: PiGemma omits internal sqrt(h)
         # scaling, so text needs explicit pre-scale; image stays raw
         # (lerobot patched_embed_image net = ×1).
-        text_hidden = self.llm_backbone.text_hidden_size
-        sqrt_h = text_hidden ** 0.5
         image_embeds_list: list[torch.Tensor] = []
         for img in images:
             img_emb = self.vision_backbone(img)
             img_emb = self.llm_backbone.multi_modal_projector(img_emb)
             image_embeds_list.append(img_emb)
-        text_embs = self.llm_backbone.embed_tokens(lang_tokens) * sqrt_h
+        text_embs = self.llm_backbone.embed_text_openpi(lang_tokens)
 
         # ─── 4. Concat into prefix (NO state token — state is in lang_tokens) ──
         prefix_embs = torch.cat([*image_embeds_list, text_embs], dim=1)
diff --git a/src/tether/runtime/fast_inference/pi05.py b/src/tether/runtime/fast_inference/pi05.py
index 9fc374ab..950bb5c0 100644
--- a/src/tether/runtime/fast_inference/pi05.py
+++ b/src/tether/runtime/fast_inference/pi05.py
@@ -799,9 +799,8 @@ def predict_action(
         )
         # Embed via the paligemma text model's embed_tokens.
         # Pi0.5 scales the embedding by sqrt(hidden_dim) — matches the FluxVLA reference.
-        text_model = self._vla.llm_backbone.model.model.language_model
-        lang_emb = text_model.embed_tokens(lang_tokens[0, :prompt_len])
-        lang_emb = (lang_emb * math.sqrt(lang_emb.shape[-1])).bfloat16()
+        lang_emb = self._vla.llm_backbone.embed_text_openpi(lang_tokens[0, :prompt_len])
+        lang_emb = lang_emb.bfloat16()
 
         chunk_size = self.n_action_steps
         if noise is None:
diff --git a/tests/_lerobot_pi_golden.py b/tests/_lerobot_pi_golden.py
new file mode 100644
index 00000000..9018e74c
--- /dev/null
+++ b/tests/_lerobot_pi_golden.py
@@ -0,0 +1,270 @@
+"""Golden pi0/pi0.5 reference from the qualified OLD stack (independent of LeRobot 0.6.1).
+
+Why: the LeRobot 0.6.1 port tests compared Tether's patched forward with
+native 0.6.1 in the same interpreter. Under transformers 5.3 native 0.6.1 is
+itself mis-scaled (image and language embeddings ~1/sqrt(2048) of openpi), so
+"matches native" passed while GPU parity failed at cosine 0.47. This fixture
+pins outputs from the stack that was GPU-qualified before the migration:
+LeRobot 0.5.1 + transformers 5.3.0 + Tether cdb94dc. Any stack that drifts
+from openpi scale fails against it.
+
+    capture (old stack):  python tests/_lerobot_pi_golden.py capture {pi0,pi05} OUT.pt
+    compare (new stack):  python tests/_lerobot_pi_golden.py compare {pi0,pi05} GOLDEN.pt
+    export  (new stack):  python tests/_lerobot_pi_golden.py export  {pi0,pi05} GOLDEN.pt DIR
+
+The tiny model shrinks constructor sizes only (two layers, width 32, two-layer
+SigLIP, vocabulary 64, 16x16 images, three-action chunk). Weights are saved in
+the fixture, so initialisation order differences between versions cannot leak in.
+"""
+
+from __future__ import annotations
+
+import sys
+
+NUM_STEPS = 10
+# float32 tolerance: the old stack divides image features by sqrt(2048) inside
+# transformers 5.3 and LeRobot 0.5.1 multiplies them back; 5.4+ skips both,
+# and language scaling moved into GemmaTextScaledWordEmbedding. Equal maths,
+# different rounding, so allow a few float32 ulps at these magnitudes.
+RTOL = 1e-5
+ATOL = 1e-5
+
+
+def _modules(family):
+    if family == "pi0":
+        from lerobot.policies.pi0 import modeling_pi0 as module
+
+        return module, module.PI0Pytorch, module.PI0Config
+    from lerobot.policies.pi05 import modeling_pi05 as module
+
+    return module, module.PI05Pytorch, module.PI05Config
+
+
+def build(family):
+    import torch
+    from unittest.mock import patch
+
+    torch.set_num_threads(1)
+    torch.manual_seed(123)
+    module, cls, config_cls = _modules(family)
+    original_pali = module.PaliGemmaForConditionalGenerationWithPiGemma
+    original_expert = module.PiGemmaForCausalLM
+
+    def tiny_pali(config):
+        config.text_config.vocab_size = 64
+        config.vision_config.hidden_size = 32
+        config.vision_config.intermediate_size = 64
+        config.vision_config.num_hidden_layers = 2
+        config.vision_config.num_attention_heads = 4
+        config.vision_config.patch_size = 4
+        config.vision_config.projection_dim = 32
+        return original_pali(config=config)
+
+    def tiny_expert(config):
+        config.vocab_size = 64
+        return original_expert(config=config)
+
+    config = config_cls(
+        device="cpu",
+        dtype="float32",
+        image_resolution=(16, 16),
+        max_action_dim=4,
+        max_state_dim=4,
+        chunk_size=3,
+        n_action_steps=3,
+        compile_model=False,
+    )
+    with (
+        patch.object(
+            module, "get_gemma_config", return_value=module.GemmaConfig(32, 2, 64, 8, 1, 4)
+        ),
+        patch.object(module, "PaliGemmaForConditionalGenerationWithPiGemma", tiny_pali),
+        patch.object(module, "PiGemmaForCausalLM", tiny_expert),
+    ):
+        model = cls(config).eval()
+    return model
+
+
+def make_inputs():
+    import torch
+
+    g = torch.Generator().manual_seed(7)
+    # Images in [-1, 1] like LeRobot's preprocessed SigLIP input.
+    return {
+        "img0": torch.rand(2, 3, 16, 16, generator=g) * 2 - 1,
+        "img1": torch.rand(2, 3, 16, 16, generator=g) * 2 - 1,
+        "img2": torch.rand(2, 3, 16, 16, generator=g) * 2 - 1,
+        "imask0": torch.tensor([True, True]),
+        "imask1": torch.tensor([True, False]),
+        "imask2": torch.tensor([False, True]),
+        "tokens": torch.randint(0, 64, (2, 4), generator=g),
+        "lmask": torch.tensor([[True, True, False, False], [True, True, True, False]]),
+        "state": torch.randn(2, 4, generator=g),
+        "noise": torch.randn(2, 3, 4, generator=g),
+        "actions": torch.randn(2, 3, 4, generator=g),
+        "time": torch.tensor([0.7, 0.3]),
+    }
+
+
+def _obs(family, x):
+    obs = [
+        [x["img0"], x["img1"], x["img2"]],
+        [x["imask0"], x["imask1"], x["imask2"]],
+        x["tokens"],
+        x["lmask"],
+    ]
+    return obs + ([x["state"]] if family == "pi0" else [])
+
+
+def outputs(family, model, x):
+    """Native outputs, then Tether's patched export forward (installs patches)."""
+    import copy
+
+    import torch
+
+    obs = _obs(family, x)
+    native = copy.deepcopy(model)
+    out = {}
+    with torch.no_grad():
+        prefix, pad, _ = native.embed_prefix(*obs[:4])
+        out["prefix_embs"] = prefix
+        out["prefix_pad"] = pad.to(torch.int64)
+        out["loss"] = native(*obs, x["actions"], x["noise"], x["time"])
+        out["actions_1"] = native.sample_actions(*obs, noise=x["noise"].clone(), num_steps=1)
+        out["actions_10"] = native.sample_actions(
+            *obs, noise=x["noise"].clone(), num_steps=NUM_STEPS
+        )
+        from tether.exporters import monolithic
+
+        monolithic.apply_export_patches()
+        if family == "pi05":
+            monolithic._apply_pi05_denoise_step_patch()
+        out["patched_actions_10"] = model.sample_actions(
+            *obs, noise=x["noise"].clone(), num_steps=NUM_STEPS
+        )
+    return out
+
+
+def capture(family, path):
+    import importlib.metadata as md
+
+    import torch
+
+    model = build(family)
+    x = make_inputs()
+    state = {k: v.clone().contiguous() for k, v in model.state_dict().items()}
+    out = outputs(family, model, x)
+    meta = {p: md.version(p) for p in ("lerobot", "transformers", "torch")}
+    torch.save({"state_dict": state, "inputs": x, "outputs": out, "meta": meta}, path)
+    print(family, "captured", meta)
+
+
+def _load(family, path):
+    import torch
+
+    golden = torch.load(path, weights_only=True)
+    model = build(family)
+    missing, unexpected = model.load_state_dict(golden["state_dict"], strict=False)
+    # Tied lm_head weights may be keyed differently across versions; anything
+    # else missing means the architectures diverged and must fail loudly.
+    assert not unexpected, unexpected
+    assert all("lm_head" in k for k in missing), missing
+    return golden, model
+
+
+def _report(name, actual, expected):
+    import torch
+
+    diff = (actual.double() - expected.double()).abs()
+    cos = torch.nn.functional.cosine_similarity(
+        actual.double().flatten(), expected.double().flatten(), dim=0
+    )
+    scale = actual.double().norm() / expected.double().norm()
+    print(
+        f"{name}: max_abs={diff.max().item():.3e} mean_abs={diff.mean().item():.3e} "
+        f"cos={cos.item():.9f} norm_ratio={scale.item():.6f}"
+    )
+
+
+def compare(family, path):
+    import torch
+
+    golden, model = _load(family, path)
+    out = outputs(family, model, golden["inputs"])
+    failures = []
+    for name, expected in golden["outputs"].items():
+        actual = out[name]
+        if actual.dtype.is_floating_point:
+            _report(name, actual, expected)
+        try:
+            torch.testing.assert_close(actual, expected, rtol=RTOL, atol=ATOL)
+        except AssertionError as exc:
+            failures.append(f"{name}: {exc}")
+    print("golden from", golden["meta"])
+    assert not failures, "\n".join(failures)
+    print(family, "golden parity passed")
+
+
+def export(family, path, out_dir):
+    """Real monolithic toolchain (onnx-diagnostic patches, torch.export,
+    torch.onnx.export, Where-dtype fix) on the tiny model, run in ONNX Runtime
+    and compared with the OLD-stack golden. Mirrors export_{pi0,pi05}_monolithic
+    minus checkpoint loading and tokenizer probing; B=1 like the exporter."""
+    from pathlib import Path
+
+    import numpy as np
+    import onnxruntime as ort
+    import torch
+    from onnx_diagnostic.torch_export_patches import torch_export_patches
+
+    from tether.exporters import monolithic
+
+    golden, model = _load(family, path)
+    x = golden["inputs"]
+    monolithic.apply_export_patches()
+    if family == "pi05":
+        monolithic._apply_pi05_denoise_step_patch()
+    monolithic._force_eager_attn(model)
+
+    class Wrapper(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.model = model
+
+        def forward(self, i0, i1, i2, m0, m1, m2, tokens, lmask, *rest):
+            kwargs = {"noise": rest[-1], "num_steps": NUM_STEPS}
+            args = [[i0, i1, i2], [m0, m1, m2], tokens, lmask, *rest[:-1]]
+            return self.model.sample_actions(*args, **kwargs)
+
+    names = ["img0", "img1", "img2", "imask0", "imask1", "imask2", "tokens", "lmask"]
+    names += (["state"] if family == "pi0" else []) + ["noise"]
+    # Row 0 of the golden batch: all three cameras present for row 0 except
+    # camera 2; B=1 matches the production exporter's trace shape.
+    feed = {n: x[n][:1].clone() for n in names}
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    onnx_path = out_dir / "model.onnx"
+    wrapper = Wrapper().eval()
+    with torch_export_patches(patch_transformers=True):
+        ep = torch.export.export(wrapper, tuple(feed.values()), dynamic_shapes=None, strict=False)
+    torch.onnx.export(
+        ep,
+        tuple(feed.values()),
+        str(onnx_path),
+        input_names=names,
+        output_names=["actions"],
+        opset_version=19,
+    )
+    monolithic._fix_onnx_where_dtype_mismatches(onnx_path)
+    session = ort.InferenceSession(str(onnx_path), providers=["CPUExecutionProvider"])
+    (actions,) = session.run(None, {n: v.numpy() for n, v in feed.items()})
+    actual = torch.from_numpy(np.asarray(actions))
+    expected = golden["outputs"]["actions_10"][:1]
+    _report("onnx_actions_10", actual, expected)
+    torch.testing.assert_close(actual, expected, rtol=1e-4, atol=1e-4)
+    print(family, "onnx export golden parity passed")
+
+
+if __name__ == "__main__":
+    mode, family = sys.argv[1], sys.argv[2]
+    {"capture": capture, "compare": compare, "export": export}[mode](family, *sys.argv[3:])
diff --git a/tests/fixtures/lerobot_pi_golden/SHA256SUMS b/tests/fixtures/lerobot_pi_golden/SHA256SUMS
new file mode 100644
index 00000000..1ba19560
--- /dev/null
+++ b/tests/fixtures/lerobot_pi_golden/SHA256SUMS
@@ -0,0 +1,2 @@
+992d9f5cedcb8d13fbe7202f584f57267df6e08ac65bee0ee3dec7cc0986bde1  pi0.pt
+3f57a88d0f220703a201581d8f1033f06aed7ba311b9e61f5ec9115a6ad69c3e  pi05.pt
diff --git a/tests/fixtures/lerobot_pi_golden/pi0.pt b/tests/fixtures/lerobot_pi_golden/pi0.pt
new file mode 100644
index 0000000000000000000000000000000000000000..917821a4c40c01c20bbb2aee9bb2bf569943f710
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diff --git a/tests/fixtures/lerobot_pi_golden/pi05.pt b/tests/fixtures/lerobot_pi_golden/pi05.pt
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literal 0
HcmV?d00001

diff --git a/tests/test_cli_export_end_to_end.py b/tests/test_cli_export_end_to_end.py
index 73e09234..eb88717f 100644
--- a/tests/test_cli_export_end_to_end.py
+++ b/tests/test_cli_export_end_to_end.py
@@ -18,11 +18,14 @@
 from __future__ import annotations
 
 import logging
+import re
 import sys
 import types
 
 import pytest
 
+from tether._lerobot_version import QUALIFIED_TRANSFORMERS_VERSION
+
 
 def test_monolithic_module_importable():
     """The module must be importable even without the [monolithic] extras
@@ -91,7 +94,18 @@ def test_dep_check_catches_wrong_transformers(monkeypatch):
     import transformers
 
     monkeypatch.setattr(transformers, "__version__", "4.99.0")
-    with pytest.raises(ImportError, match="5.3.0"):
+    with pytest.raises(ImportError, match=re.escape(QUALIFIED_TRANSFORMERS_VERSION)):
+        monolithic._require_monolithic_deps()
+
+
+def test_dep_check_refuses_transformers_53(monkeypatch):
+    """5.3.0 + LeRobot 0.6.1 mis-scales pi0/pi0.5 embeddings ~45x (GPU parity
+    cosine 0.47, Studio ADR 0003 correction 2026-10-12); the gate must refuse it."""
+    from tether.exporters import monolithic
+    import transformers
+
+    monkeypatch.setattr(transformers, "__version__", "5.3.0")
+    with pytest.raises(ImportError, match="mis-scaled"):
         monolithic._require_monolithic_deps()
 
 
@@ -102,7 +116,7 @@ def test_dep_check_catches_wrong_lerobot(monkeypatch, lerobot_version):
     from tether.exporters import monolithic
     import transformers
 
-    monkeypatch.setattr(transformers, "__version__", "5.3.0")
+    monkeypatch.setattr(transformers, "__version__", QUALIFIED_TRANSFORMERS_VERSION)
     for mod_name in ("lerobot", "onnx_diagnostic", "onnxscript", "optree", "scipy"):
         monkeypatch.setitem(sys.modules, mod_name, types.ModuleType(mod_name))
 
diff --git a/tests/test_lerobot_pi_golden.py b/tests/test_lerobot_pi_golden.py
new file mode 100644
index 00000000..cd4c99c4
--- /dev/null
+++ b/tests/test_lerobot_pi_golden.py
@@ -0,0 +1,73 @@
+"""pi0/pi0.5 against a golden captured on the pre-migration stack (see _lerobot_pi_golden.py).
+
+The fixture was captured with LeRobot 0.5.1 + transformers 5.3.0 + torch 2.7.1
+and Tether cdb94dc (the GPU-qualified stack before ADR 0003):
+
+    PYTHONPATH=<tether@cdb94dc>/src <venv-0.5.1>/bin/python \\
+        tests/_lerobot_pi_golden.py capture {pi0,pi05} tests/fixtures/lerobot_pi_golden/{pi0,pi05}.pt
+"""
+
+from __future__ import annotations
+
+import hashlib
+import importlib.metadata
+import importlib.util
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+HERE = Path(__file__).resolve().parent
+FIXTURES = HERE / "fixtures" / "lerobot_pi_golden"
+HELPER = HERE / "_lerobot_pi_golden.py"
+FAMILIES = ["pi0", "pi05"]
+
+
+def _require_lerobot_061():
+    try:
+        version = importlib.metadata.version("lerobot")
+    except importlib.metadata.PackageNotFoundError:
+        pytest.skip("requires the LeRobot 0.6.1 native extra")
+    if version != "0.6.1":
+        pytest.skip("this golden check exercises LeRobot 0.6.1")
+
+
+def _run(*args, timeout=600):
+    result = subprocess.run(
+        [sys.executable, str(HELPER), *args], capture_output=True, text=True, timeout=timeout
+    )
+    assert result.returncode == 0, result.stdout + result.stderr
+    return result.stdout
+
+
+def test_golden_fixture_provenance():
+    torch = pytest.importorskip("torch")
+    sums = dict(
+        reversed(line.split()) for line in (FIXTURES / "SHA256SUMS").read_text().splitlines()
+    )
+    for family in FAMILIES:
+        path = FIXTURES / f"{family}.pt"
+        assert hashlib.sha256(path.read_bytes()).hexdigest() == sums[path.name]
+        meta = torch.load(path, weights_only=True)["meta"]
+        assert meta == {"lerobot": "0.5.1", "transformers": "5.3.0", "torch": "2.7.1"}
+
+
+@pytest.mark.parametrize("family", FAMILIES)
+def test_native_and_patched_match_pre_migration_golden(family):
+    _require_lerobot_061()
+    out = _run("compare", family, str(FIXTURES / f"{family}.pt"))
+    assert f"{family} golden parity passed" in out
+
+
+@pytest.mark.parametrize("family", FAMILIES)
+def test_monolithic_onnx_export_matches_pre_migration_golden(family, tmp_path):
+    _require_lerobot_061()
+    torch = pytest.importorskip("torch")
+    if tuple(int(p) for p in torch.__version__.split(".")[:2]) < (2, 8):
+        pytest.skip("onnx-diagnostic export patches need torch>=2.8")
+    for module in ("onnx_diagnostic", "onnxruntime", "onnxscript"):
+        if importlib.util.find_spec(module) is None:
+            pytest.skip(f"requires the monolithic extra ({module})")
+    out = _run("export", family, str(FIXTURES / f"{family}.pt"), str(tmp_path), timeout=900)
+    assert f"{family} onnx export golden parity passed" in out
diff --git a/tests/test_paligemma_backbone.py b/tests/test_paligemma_backbone.py
index 31344857..7bf35768 100644
--- a/tests/test_paligemma_backbone.py
+++ b/tests/test_paligemma_backbone.py
@@ -215,3 +215,25 @@ def __init__(self):
             )
 
     return _Stub()
+
+
+@pytest.mark.parametrize("module_scale", [1.0, 8.0**0.5])
+def test_embed_text_openpi_is_independent_of_embedding_module_scaling(module_scale):
+    """transformers 5.3 Gemma embeds unscaled; 5.4+ GemmaTextScaledWordEmbedding
+    already multiplies by sqrt(hidden). openpi text = table lookup * sqrt(hidden)
+    either way, never sqrt(hidden) twice."""
+    stub = _make_stub_paligemma(text_hidden=8, vocab_size=16)
+    table = stub.model.language_model.embed_tokens
+
+    class Scaled(nn.Embedding):
+        def forward(self, ids):
+            return super().forward(ids) * module_scale
+
+    scaled = Scaled(16, 8)
+    scaled.weight = table.weight
+    stub.model.language_model.embed_tokens = scaled
+    ids = torch.tensor([[1, 5, 15]])
+    expected = table.weight[ids] * 8.0**0.5
+    torch.testing.assert_close(
+        PaliGemmaBackbone(model=stub).embed_text_openpi(ids), expected, rtol=0, atol=0
+    )
diff --git a/tests/test_pi05_vla.py b/tests/test_pi05_vla.py
index 352ffffb..914e05a9 100644
--- a/tests/test_pi05_vla.py
+++ b/tests/test_pi05_vla.py
@@ -22,6 +22,7 @@
 from tether.models.base_vla import BaseVLA
 from tether.models.heads import VLAHead
 from tether.models.llm import LLMBackbone
+from tether.models.llm.paligemma_backbone import PaliGemmaBackbone
 from tether.models.vision import VisionBackbone
 from tether.models.vlas.pi05 import Pi05VLA
 from tether.registry.components import VLAS
@@ -231,6 +232,8 @@ def forward(self, images):
 class _StubPaliGemmaForPi05(LLMBackbone, nn.Module):
     """Stub PaliGemma — exposes the attrs Pi05VLA.predict_action uses."""
 
+    embed_text_openpi = PaliGemmaBackbone.embed_text_openpi
+
     def __init__(
         self,
         text_hidden: int = 2048,
diff --git a/tests/test_pi0_vla.py b/tests/test_pi0_vla.py
index 71271c60..4cd83103 100644
--- a/tests/test_pi0_vla.py
+++ b/tests/test_pi0_vla.py
@@ -23,6 +23,7 @@
 from tether.models.base_vla import BaseVLA
 from tether.models.heads import VLAHead
 from tether.models.llm import LLMBackbone
+from tether.models.llm.paligemma_backbone import PaliGemmaBackbone
 from tether.models.projectors import Projector
 from tether.models.vision import VisionBackbone
 from tether.models.vlas.pi0 import Pi0VLA
@@ -271,6 +272,8 @@ class _StubPaliGemmaForPi0(LLMBackbone, nn.Module):
     """Stub PaliGemma exposing the property accessors Pi0VLA.predict_action
     relies on. Returns past_key_values that look like a transformers Cache."""
 
+    embed_text_openpi = PaliGemmaBackbone.embed_text_openpi
+
     def __init__(
         self,
         text_hidden: int = 2048,

From c93e852acf6669477448d7b1c22d528c01796231 Mon Sep 17 00:00:00 2001
From: RomirJ <playindus@gmail.com>
Date: Mon, 12 Oct 2026 17:49:32 -0700
Subject: [PATCH 8/8] Format two files the quality ratchet flagged as new debt

ruff format on src/tether/finetune/resume.py and
tests/test_finetune_local_dataset.py (lines added earlier on this branch).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 src/tether/finetune/resume.py        | 10 +++++++++-
 tests/test_finetune_local_dataset.py |  4 +++-
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/tether/finetune/resume.py b/src/tether/finetune/resume.py
index 0fe57165..6d981110 100644
--- a/src/tether/finetune/resume.py
+++ b/src/tether/finetune/resume.py
@@ -47,7 +47,15 @@ def resolve_resume_checkpoint(output_dir: Path) -> tuple[Path, Path]:
     except (OSError, ValueError) as exc:
         raise ResumeConfigurationError(f"resume train_config.json is unreadable: {config}") from exc
     if isinstance(saved, dict) and any(
-        key in saved for key in ("eval_freq", "use_rabc", "rabc_progress_path", "rabc_kappa", "rabc_epsilon", "rabc_head_mode")
+        key in saved
+        for key in (
+            "eval_freq",
+            "use_rabc",
+            "rabc_progress_path",
+            "rabc_kappa",
+            "rabc_epsilon",
+            "rabc_head_mode",
+        )
     ):
         raise ResumeConfigurationError(
             "cross-version-resume-unsupported: LeRobot 0.5.x training configs "
diff --git a/tests/test_finetune_local_dataset.py b/tests/test_finetune_local_dataset.py
index 186a8d4e..1a678224 100644
--- a/tests/test_finetune_local_dataset.py
+++ b/tests/test_finetune_local_dataset.py
@@ -784,5 +784,7 @@ def test_pinned_lerobot_sources_match_installed_qualified_runtime():
     if dist.version != QUALIFIED_LEROBOT_VERSION:
         pytest.skip(f"lerobot {dist.version} is not the qualified {QUALIFIED_LEROBOT_VERSION}")
     pins = {**local_dataset.WRITER_FILES, **local_dataset.NORMALIZATION_FILES}
-    actual = {name: hashlib.sha256(Path(dist.locate_file(name)).read_bytes()).hexdigest() for name in pins}
+    actual = {
+        name: hashlib.sha256(Path(dist.locate_file(name)).read_bytes()).hexdigest() for name in pins
+    }
     assert actual == pins