diff --git a/FlagEmbedding/abc/finetune/embedder/AbsModeling.py b/FlagEmbedding/abc/finetune/embedder/AbsModeling.py
index 3e9e32cb3..3bff951ec 100644
--- a/FlagEmbedding/abc/finetune/embedder/AbsModeling.py
+++ b/FlagEmbedding/abc/finetune/embedder/AbsModeling.py
@@ -270,7 +270,7 @@ def forward(
 
         if self.training:
             if teacher_scores is not None:
-                teacher_scores = torch.tensor(teacher_scores, device=device)
+                teacher_scores = torch.tensor(teacher_scores, dtype=torch.float32, device=device)
                 teacher_scores = teacher_scores.view(batch_size, -1).detach()   # (batch_size, group_size)
                 teacher_targets = F.softmax(teacher_scores, dim=-1)  # (batch_size, group_size)
             else:
diff --git a/FlagEmbedding/finetune/embedder/encoder_only/m3/modeling.py b/FlagEmbedding/finetune/embedder/encoder_only/m3/modeling.py
index c3111ea5e..739690e01 100644
--- a/FlagEmbedding/finetune/embedder/encoder_only/m3/modeling.py
+++ b/FlagEmbedding/finetune/embedder/encoder_only/m3/modeling.py
@@ -394,7 +394,7 @@ def forward(
 
         if self.training:
             if teacher_scores is not None:
-                teacher_scores = torch.tensor(teacher_scores, device=q_dense_vecs.device)
+                teacher_scores = torch.tensor(teacher_scores, dtype=torch.float32, device=q_dense_vecs.device)
                 teacher_scores = teacher_scores.view(q_dense_vecs.size(0), -1).detach()   # (batch_size, group_size)
                 teacher_targets = F.softmax(teacher_scores, dim=-1)  # (batch_size, group_size)
             else:
diff --git a/tests/test_embedder_teacher_scores.py b/tests/test_embedder_teacher_scores.py
new file mode 100644
index 000000000..0abc04a6b
--- /dev/null
+++ b/tests/test_embedder_teacher_scores.py
@@ -0,0 +1,117 @@
+"""Regression tests for integer teacher scores in embedder distillation."""
+
+from copy import deepcopy
+
+import pytest
+import torch
+from tokenizers import Tokenizer
+from tokenizers.models import WordLevel
+from transformers import PreTrainedTokenizerFast, XLMRobertaConfig, XLMRobertaModel
+
+from FlagEmbedding.finetune.embedder.encoder_only.base.modeling import (
+    BiEncoderOnlyEmbedderModel,
+)
+from FlagEmbedding.finetune.embedder.encoder_only.m3.modeling import (
+    EncoderOnlyEmbedderM3Model,
+)
+
+
+@pytest.fixture(params=["base", "m3_dense", "m3_unified"])
+def embedder(request):
+    torch.manual_seed(42)
+    config = XLMRobertaConfig(
+        vocab_size=32,
+        hidden_size=8,
+        intermediate_size=16,
+        num_hidden_layers=1,
+        num_attention_heads=2,
+        max_position_embeddings=16,
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+    )
+    encoder = XLMRobertaModel(config)
+    if request.param == "base":
+        return BiEncoderOnlyEmbedderModel(encoder)
+
+    tokenizer = PreTrainedTokenizerFast(
+        tokenizer_object=Tokenizer(
+            WordLevel(
+                {
+                    "<s>": 0,
+                    "<pad>": 1,
+                    "</s>": 2,
+                    "<unk>": 3,
+                },
+                unk_token="<unk>",
+            )
+        ),
+        cls_token="<s>",
+        pad_token="<pad>",
+        eos_token="</s>",
+        unk_token="<unk>",
+    )
+    return EncoderOnlyEmbedderM3Model(
+        base_model={
+            "model": encoder,
+            "sparse_linear": torch.nn.Linear(config.hidden_size, 1),
+            "colbert_linear": torch.nn.Linear(config.hidden_size, config.hidden_size),
+        },
+        tokenizer=tokenizer,
+        unified_finetuning=request.param == "m3_unified",
+    )
+
+
+@pytest.mark.parametrize("kd_loss_type", ["kl_div", "m3_kd_loss"])
+@pytest.mark.parametrize("teacher_scores", [[2, 0, -1, 3], [2, 0.5, -1, 3]])
+def test_integer_teacher_scores_match_float_loss_and_gradients(
+    embedder, kd_loss_type, teacher_scores
+):
+    embedder.kd_loss_type = kd_loss_type
+    float_embedder = deepcopy(embedder)
+    queries = {
+        "input_ids": torch.tensor([[0, 4, 5, 2, 1], [0, 6, 7, 8, 2]]),
+        "attention_mask": torch.tensor([[1, 1, 1, 1, 0], [1, 1, 1, 1, 1]]),
+    }
+    passages = {
+        "input_ids": torch.tensor(
+            [
+                [0, 4, 9, 2, 1],
+                [0, 10, 11, 2, 1],
+                [0, 6, 12, 13, 2],
+                [0, 14, 15, 2, 1],
+            ]
+        ),
+        "attention_mask": torch.tensor(
+            [
+                [1, 1, 1, 1, 0],
+                [1, 1, 1, 1, 0],
+                [1, 1, 1, 1, 1],
+                [1, 1, 1, 1, 0],
+            ]
+        ),
+    }
+
+    expected_loss = float_embedder(
+        queries=queries,
+        passages=passages,
+        teacher_scores=[float(score) for score in teacher_scores],
+    ).loss
+    loss = embedder(
+        queries=queries,
+        passages=passages,
+        teacher_scores=teacher_scores,
+    ).loss
+    assert torch.isfinite(loss)
+    torch.testing.assert_close(loss, expected_loss)
+
+    loss.backward()
+    expected_loss.backward()
+    for (name, param), (expected_name, expected_param) in zip(
+        embedder.named_parameters(), float_embedder.named_parameters()
+    ):
+        assert name == expected_name
+        if expected_param.grad is None:
+            assert param.grad is None
+        else:
+            assert torch.isfinite(param.grad).all()
+            torch.testing.assert_close(param.grad, expected_param.grad)