Update RL research progress

README.md

@@ -32,6 +32,8 @@ Trinity-RFT provides functionalities for users with different backgrounds and ob
 
 ## 🚀 News
 
+* [2026-09] Introducing [CoD ("Connect the Dots")](https://arxiv.org/pdf/2606.20002): training LLM agents by RL for cross-task adaptation and continual learning at deployment.
+* [2026-08] 🎉 [R3L](https://github.com/shiweijiezero/R3L) gets accepted by EMNLP 2026.
 * [2026-06] [[Release Notes]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.6.0) Trinity-RFT v0.6.0 released: SGLang support, optimized fully async weights synchronization and scheduling to reduce bubbles, improved MoE training stability, and verl 0.8.0 upgrade.
 * [2026-06] 🎉 Two papers accepted by ICML 2026: [Learn-to-Ask](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/learn_to_ask), and [LLM-RL entropy dynamics](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/entropy).
 * [2026-04] [[Release Notes]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.5.2) Trinity-RFT v0.5.2 released: support for Qwen3.5 series, bug fixes and optimizations.

README_zh.md

@@ -41,6 +41,8 @@ Trinity-RFT 面向不同背景和目标的用户提供相应功能：
 
 ## 🚀 新闻
 
+* [2026-09] 推出 [CoD ("Connect the Dots")](https://arxiv.org/pdf/2606.20002)：用强化学习教会 LLM 智能体如何在部署阶段跨任务持续学习。
+* [2026-08] 🎉 [R3L](https://github.com/shiweijiezero/R3L) 论文被 EMNLP 2026 接收。
 * [2026-06] [[发布说明]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.6.0) Trinity-RFT v0.6.0 发布：支持 SGLang，优化全异步参数同步以及调度策略以减少空泡，优化 MoE 模型训练稳定性，升级到 verl 0.8.0。
 * [2026-06] 🎉 两篇论文被 ICML 2026 接收：[Learn-to-Ask](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/learn_to_ask) 和 [LLM-RL 熵动力学](https://github.com/agentscope-ai/Trinity-RFT/tree/main/examples/entropy)。
 * [2026-04] [[发布说明]](https://github.com/agentscope-ai/Trinity-RFT/releases/tag/v0.5.2) Trinity-RFT v0.5.2 发布：支持 Qwen3.5 系列，修复 Bug 并进行多项优化。

examples/research_cod/README.md

@@ -10,11 +10,11 @@ CoD groups related tasks into a pack and generates a long rollout trajectory int
 The whole pack is trained end-to-end with RL, with fine-grained credit assignment rewarding context updates that make future tasks easier.
 A trained model's reward rises across pack positions, which is the signature of the elicited CoD meta-capability.
 
-> **The full implementation lives on the [`research/cod`](https://github.com/agentscope-ai/Trinity-RFT/tree/research/cod/examples/research_cod) branch.** Check out that branch to view or run the full code.
+> **The full implementation lives on the [`research/cod_v2`](https://github.com/agentscope-ai/Trinity-RFT/tree/research/cod_v2/examples/research_cod) branch.** Check out that branch to view or run the full code.
 
 <p align="center">
   <img src="assets/cod_overview.png" alt="CoD overview" width="760">
-  <br><sub><em>Figure 1: a visualization of CoD-Deploy and CoD-Train (compared with standard task-by-task RL). Environments A and B are used for training; M is a new environment for deployment or evaluation. Each block is one rollout episode, for solving a task x_i (which may itself be a long-horizon multi-turn task) or for updating the agent's context z_i about the current environment.</em></sub>
+  <br><sub><em>Figure: a visualization of CoD-Deploy and CoD-Train (compared with standard task-by-task RL). Environments A and B are used for training; M is a new environment for deployment or evaluation. Each block is one rollout episode, for solving a task x_i (which may itself be a long-horizon multi-turn task) or for updating the agent's context z_i about the current environment.</em></sub>
 </p>
 
 ---
@@ -40,7 +40,12 @@ Key metric: `reward_iterative_hint_e2e_taskset_{ts}_pos_{pos}`, the mean reward
 
 <p align="center">
   <img src="assets/cod_effect.png" alt="reward rising across pack positions" width="820">
-  <br><sub><em>Figure 2: the CoD effect. Reward rises across pack positions during training and at OOD evaluation, both in-domain (harder FrozenLake) and cross-domain (Alchemy, Terminal).</em></sub>
+  <br><sub><em>Figure: the CoD effect. Reward rises across pack positions during training and at OOD evaluation, both in-domain (harder FrozenLake) and cross-domain (Alchemy, Terminal). Qwen3-8B is used as the initial model.</em></sub>
+</p>
+
+<p align="center">
+  <img src="assets/cod_qwen27b.png" alt="reward rising across pack positions" width="820">
+  <br><sub><em>Figure: scaling CoD to larger models and harder environments.</em></sub>
 </p>
 
 ---
@@ -56,14 +61,14 @@ In each environment, the tasks in a pack share something reusable: sometimes a h
 | Terminal | `cod_terminal_workflow` | How commands and paths work and their pitfalls, and roughly where files live |
 | Learn2Ask | `cod_learn2ask_workflow` | When to keep asking vs. when to stop and give a diagnosis |
 
-The workflow implementations live under [`trinity/common/workflows/connect_the_dots/`](https://github.com/agentscope-ai/Trinity-RFT/tree/research/cod/trinity/common/workflows/connect_the_dots) on the `research/cod` branch.
+The workflow implementations live under [`trinity/common/workflows/connect_the_dots/`](https://github.com/agentscope-ai/Trinity-RFT/tree/research/cod_v2/trinity/common/workflows/connect_the_dots) on the `research/cod_v2` branch.
 
 ---
 
 ## Run it
 
 ```bash
-git clone -b research/cod https://github.com/agentscope-ai/Trinity-RFT.git
+git clone -b research/cod_v2 https://github.com/agentscope-ai/Trinity-RFT.git
 cd Trinity-RFT
 conda create -n trinity python=3.12 && conda activate trinity
 pip install -e ".[vllm,flash_attn]"

examples/research_cod/README_zh.md

@@ -10,11 +10,11 @@ CoD 把相关任务打成一个 pack（任务包），作为一条交替进行**
 整个 pack 用 RL 端到端训练，细粒度的信用分配让“使后续任务更好解的 context 更新”获得奖励。
 训练好的模型 reward 随 pack 位置递增，这正是 CoD 元能力被激发出来的标志。
 
-> **完整实现在 [`research/cod`](https://github.com/agentscope-ai/Trinity-RFT/tree/research/cod/examples/research_cod) 分支**，请切到该分支查看或运行完整代码。
+> **完整实现在 [`research/cod_v2`](https://github.com/agentscope-ai/Trinity-RFT/tree/research/cod_v2/examples/research_cod) 分支**，请切到该分支查看或运行完整代码。
 
 <p align="center">
   <img src="assets/cod_overview.png" alt="CoD 方法总览" width="760">
-  <br><sub><em>图 1：CoD-Deploy 与 CoD-Train 的示意（与标准的逐任务 RL 对比）。环境 A、B 用于训练，M 是部署或评测的新环境。每个 block 是一个 rollout 回合：求解任务 x_i（其本身可能是长程多轮任务），或更新 agent 对当前环境的 context z_i。</em></sub>
+  <br><sub><em>图：CoD-Deploy 与 CoD-Train 的示意（与标准的逐任务 RL 对比）。环境 A、B 用于训练，M 是部署或评测的新环境。每个 block 是一个 rollout 回合：求解任务 x_i（其本身可能是长程多轮任务），或更新 agent 对当前环境的 context z_i。</em></sub>
 </p>
 
 ---
@@ -40,7 +40,12 @@ pack = [ task0, task1, task2, task3 ]      # 同一 taskset 的一包相关任
 
 <p align="center">
   <img src="assets/cod_effect.png" alt="reward 随 pack 位置递增" width="820">
-  <br><sub><em>图 2：CoD 效应。reward 随 pack 位置上升，训练时如此，OOD 评测时也如此，包括 in-domain（更难的 FrozenLake）与 cross-domain（Alchemy、Terminal）。</em></sub>
+  <br><sub><em>图：CoD 效应。reward 随 pack 位置上升，训练时如此，OOD 评测时也如此，包括 in-domain（更难的 FrozenLake）与 cross-domain（Alchemy、Terminal）。使用 Qwen3-8B 作为初始模型。</em></sub>
+</p>
+
+<p align="center">
+  <img src="assets/cod_qwen27b.png" alt="reward rising across pack positions" width="820">
+  <br><sub><em>图：将 CoD 扩展到更大的模型与更困难的环境。</em></sub>
 </p>
 
 ---
@@ -56,14 +61,14 @@ pack = [ task0, task1, task2, task3 ]      # 同一 taskset 的一包相关任
 | Terminal | `cod_terminal_workflow` | 命令、路径的用法与易踩的坑，以及文件大致在哪 |
 | Learn2Ask | `cod_learn2ask_workflow` | 何时继续追问、何时停下来给诊断 |
 
-各环境的 workflow 实现位于 `research/cod` 分支的 [`trinity/common/workflows/connect_the_dots/`](https://github.com/agentscope-ai/Trinity-RFT/tree/research/cod/trinity/common/workflows/connect_the_dots)。
+各环境的 workflow 实现位于 `research/cod_v2` 分支的 [`trinity/common/workflows/connect_the_dots/`](https://github.com/agentscope-ai/Trinity-RFT/tree/research/cod_v2/trinity/common/workflows/connect_the_dots)。
 
 ---
 
 ## 运行
 
 ```bash
-git clone -b research/cod https://github.com/agentscope-ai/Trinity-RFT.git
+git clone -b research/cod_v2 https://github.com/agentscope-ai/Trinity-RFT.git
 cd Trinity-RFT
 conda create -n trinity python=3.12 && conda activate trinity
 pip install -e ".[vllm,flash_attn]"