Warn when truncated sampling biases the vLLM importance-sampling ratio

tests/test_grpo_trainer.py

@@ -2688,6 +2688,54 @@ def dummy_reward_func(completions, **kwargs):
         expected_warning = "weights each sequence by its completion length"
         assert (expected_warning in caplog.text) == (loss_type != "grpo")
 
+    @pytest.mark.parametrize(
+        ("top_p", "top_k", "min_p", "should_warn"),
+        [
+            (1.0, 0, None, False),  # defaults: nothing is truncated, the two distributions agree
+            (0.9, 0, None, True),
+            (1.0, 50, None, True),
+            (1.0, 0, 0.05, True),
+            (0.9, 50, 0.05, True),
+        ],
+    )
+    def test_warning_raised_truncated_sampling_with_importance_sampling_correction(
+        self, top_p, top_k, min_p, should_warn
+    ):
+        """Truncated sampling biases the vLLM importance-sampling ratio.
+
+        vLLM returns logprobs renormalized over the surviving support while the trainer takes a full-vocab log-softmax,
+        so their difference carries `log S`, the log of the mass that survives truncation, on top of the
+        train/inference mismatch the correction is meant to measure. Warn instead of correcting silently.
+        """
+        with warnings.catch_warnings(record=True) as caught:
+            warnings.simplefilter("always")
+            GRPOConfig(
+                output_dir=self.tmp_dir,
+                use_vllm=True,
+                vllm_importance_sampling_correction=True,
+                top_p=top_p,
+                top_k=top_k,
+                min_p=min_p,
+                report_to="none",
+            )
+
+        messages = [str(w.message) for w in caught]
+        assert any("biases `sampling/sampling_logp_difference`" in m for m in messages) == should_warn
+
+    def test_no_truncation_warning_without_importance_sampling_correction(self):
+        """The bias only exists when the correction consumes vLLM's logprobs, so truncation alone must stay silent."""
+        with warnings.catch_warnings(record=True) as caught:
+            warnings.simplefilter("always")
+            GRPOConfig(
+                output_dir=self.tmp_dir,
+                use_vllm=True,
+                vllm_importance_sampling_correction=False,
+                top_p=0.9,
+                report_to="none",
+            )
+
+        assert not any("biases `sampling/sampling_logp_difference`" in str(w.message) for w in caught)
+
     def test_train_num_generations_larger_than_batch_size(self):
         dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
 

trl/trainer/grpo_config.py

@@ -1134,6 +1134,34 @@ def __post_init__(self):
             )
             self.vllm_importance_sampling_clip_max = self.vllm_importance_sampling_cap
 
+        if self.use_vllm and self.vllm_importance_sampling_correction:
+            # vLLM is asked for `processed_logprobs`, which are renormalized over the support that survives
+            # top_p/top_k/min_p, while the trainer takes a full-vocab log-softmax. Their difference therefore carries
+            # log(S), where S is the surviving mass, on top of the train/inference mismatch the correction exists to
+            # measure. Measured on one H100: `sampling/sampling_logp_difference/mean` reads |log(top_p)| exactly, i.e.
+            # 0.105 at top_p=0.9 and 0.223 at top_p=0.8 against 0.001 at top_p=1.0.
+            truncating = [
+                name
+                for name, active in (
+                    ("top_p", self.top_p < 1.0),
+                    ("top_k", self.top_k > 0),
+                    ("min_p", self.min_p is not None and self.min_p > 0.0),
+                )
+                if active
+            ]
+            if truncating:
+                warnings.warn(
+                    f"{' and '.join(truncating)} truncates sampling, which biases "
+                    "`sampling/sampling_logp_difference` and the importance-sampling ratio derived from it: vLLM "
+                    "renormalizes its logprobs over the surviving tokens while the trainer normalizes over the full "
+                    "vocabulary, so their difference includes the log of the surviving probability mass. Set "
+                    "`vllm_importance_sampling_correction=False`, or keep the sampling defaults (`top_p=1.0`, "
+                    "`top_k=0`, `min_p=None`), until the correction accounts for truncation. See "
+                    "https://github.com/huggingface/trl/issues/6789.",
+                    UserWarning,
+                    stacklevel=3,
+                )
+
         if (
             self.vllm_importance_sampling_clip_min is not None
             and self.vllm_importance_sampling_clip_max is not None