diff --git a/tests/common/vllm_test.py b/tests/common/vllm_test.py
index bcedf2e86e..a1d47a48ff 100644
--- a/tests/common/vllm_test.py
+++ b/tests/common/vllm_test.py
@@ -199,6 +199,7 @@ async def test_generate(
         self.assertTrue(
             torch.equal(result_dict["assistant_masks"][0][prompt_length:], exp.action_mask)
         )
+        self.assertTrue(exp.logprobs.shape[0] == exp.tokens.shape[0] - prompt_length)
         self.assertTrue(torch.equal(result_dict["input_ids"][0], exp.tokens))
         self.assertRaises(ValueError, self.model_wrapper.get_openai_client)
         if self.config.explorer.rollout_model.enable_history:
diff --git a/trinity/common/models/vllm_model.py b/trinity/common/models/vllm_model.py
index 7cedf86687..5d6ff40bd2 100644
--- a/trinity/common/models/vllm_model.py
+++ b/trinity/common/models/vllm_model.py
@@ -172,7 +172,8 @@ async def generate(self, prompt: str, **kwargs) -> Sequence[Experience]:
         return experiences
 
     async def logprobs(self, token_ids: List[int]) -> torch.Tensor:
-        """Calculate the logprobs of the given tokens in async.
+        """Calculate the logprobs of the given tokens in async. Please slice the result carefully
+        to align with the actual response length.
 
         Args:
             token_ids (List[int]): The input token ids (seq_length).
@@ -217,11 +218,11 @@ async def convert_messages_to_experience(self, messages: List[dict]) -> Experien
             self.chat_template = self.tokenizer.get_chat_template()
         token_ids, action_mask, prompt_length = self.action_mask_method(
             self.tokenizer, messages, self.chat_template
-        )
-        logprobs = await self.logprobs(token_ids=token_ids.tolist())
+        )  # (seq_length, ), (seq_length, )
+        logprobs = await self.logprobs(token_ids=token_ids.tolist())  # (seq_length - 1,)
         return Experience(
             tokens=token_ids,
-            logprobs=logprobs,
+            logprobs=logprobs[prompt_length - 1 :],
             prompt_length=prompt_length,
             action_mask=action_mask[prompt_length:],  # Exclude the prompt tokens
         )