feat: Implement Two-Sided Clipping for GRPO Trainer

tests/test_grpo_trainer.py

@@ -1147,3 +1147,84 @@ def test_training_num_generations_larger_than_batch_size(self):
             for n, param in previous_trainable_params.items():
                 new_param = trainer.model.get_parameter(n)
                 self.assertFalse(torch.equal(param, new_param), f"Parameter {n} has not changed.")
+
+    @staticmethod
+    def _make_delta_trainer(tmp_dir, tokenizer, dataset):
+        """Helper method to create a GRPOTrainer with specific delta clipping parameters."""
+        cfg = GRPOConfig(
+            output_dir=tmp_dir,
+            epsilon=0.20,
+            delta=2.0,
+            epsilon_high=0.20,
+            beta=0.0,
+            loss_type="bnpo",
+            max_completion_length=2,
+            report_to="none",
+        )
+        return GRPOTrainer(
+            model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
+            args=cfg,
+            reward_funcs=lambda x, **y: [1.0] * len(x),
+            train_dataset=dataset,
+            processing_class=tokenizer,
+        )
+
+    @staticmethod
+    def _delta_inputs(device):
+        """Helper method to create standard inputs for delta clipping tests."""
+        return {
+            "prompt_ids": torch.tensor([[101]], device=device),
+            "prompt_mask": torch.tensor([[1]], device=device),
+            "completion_ids": torch.tensor([[2000, 2001]], device=device),
+            "completion_mask": torch.tensor([[1, 1]], device=device),
+        }
+
+    @parameterized.expand(
+        [
+            # name, advantage, old_prob, new_prob, expected_loss
+            ("pos_ratio_in_clip", 2.0, 0.50, 0.55, -2.2),
+            ("pos_ratio_above_clip", 2.0, 0.40, 0.60, -2.4),
+            ("neg_ratio_in_clip", -2.0, 0.50, 0.45, 1.8),
+            ("neg_ratio_below_clip", -2.0, 0.50, 0.35, 1.6),
+            ("neg_ratio_above_delta", -2.0, 0.20, 0.50, 4.0),
+            ("neg_between_clip_delta", -2.0, 0.40, 0.60, 3.0),
+        ]
+    )
+    def test_two_sided_clipping_loss(self, name, advantage, old_prob, new_prob, expected_loss):
+        """Test two-sided GRPO clipping logic with different scenarios.
+
+        Args:
+            name: Test case name
+            advantage: Advantage value for the scenario
+            old_prob: Old policy probability
+            new_prob: New policy probability
+            expected_loss: Expected loss value
+        """
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
+            tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+            dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train[:1]")
+
+            trainer = self._make_delta_trainer(tmp_dir, tokenizer, dataset)
+
+            inputs = self._delta_inputs(trainer.accelerator.device)
+            inputs.update(
+                {
+                    "advantages": torch.tensor([advantage], device=trainer.accelerator.device),
+                    "old_per_token_logps": torch.log(torch.tensor([[old_prob]], device=trainer.accelerator.device)),
+                }
+            )
+
+            # Mock _get_per_token_logps to return predefined new log probabilities
+            with patch.object(trainer, "_get_per_token_logps") as mock_logps_func:
+                mock_logps_func.return_value = torch.log(torch.tensor([[new_prob]], device=trainer.accelerator.device))
+
+                # Compute loss and verify
+                loss = trainer.compute_loss(trainer.model, inputs)
+                self.assertAlmostEqual(
+                    loss.item(),
+                    expected_loss,
+                    delta=1e-5,
+                    msg=f"Scenario {name} failed: expected {expected_loss}, got {loss.item()}",
+                )

trl/trainer/grpo_config.py

@@ -136,6 +136,8 @@ class GRPOConfig(TrainingArguments):
             Number of iterations per batch (denoted as μ in the algorithm).
         epsilon (`float`, *optional*, defaults to `0.2`):
             Epsilon value for clipping.
+        delta: (`float`, *optional*, defaults to `None`):
+            Delta value for the upper clipping bound in two-sided GRPO. Recommended to be > 1 + epsilon. This method was introduced in the [INTELLECT-2 tech report](https://huggingface.co/papers/2505.07291).
         epsilon_high (`float` or `None`, *optional*, defaults to `None`):
             Upper-bound epsilon value for clipping. If not specified, it defaults to the same value as the lower-bound
             specified in argument `epsilon`. Paper [DAPO](https://huggingface.co/papers/2503.14476) recommends `0.28`.
@@ -389,6 +391,12 @@ class GRPOConfig(TrainingArguments):
         default=0.2,
         metadata={"help": "Epsilon value for clipping."},
     )
+    delta: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "If set to a float value (e.g., 2.0), enables the upper clipping bound in two-sided GRPO loss. If None (default), the standard GRPO clipping is used. Recommended to be > 1 + epsilon when enabled."
+        },
+    )
     epsilon_high: Optional[float] = field(
         default=None,
         metadata={
@@ -536,3 +544,5 @@ def __post_init__(self):
                     "current global eval batch size, the valid values for the number of generations are: "
                     f"{possible_values}."
                 )
+        if self.delta is not None and self.use_liger_loss:
+            raise ValueError("Liger loss does not support two-sided GRPO loss yet.")

trl/trainer/grpo_trainer.py

@@ -1347,7 +1347,14 @@ def _compute_loss(self, model, inputs):
         )
         coef_1 = torch.exp(per_token_logps - old_per_token_logps)
         coef_2 = torch.clamp(coef_1, 1 - self.epsilon_low, 1 + self.epsilon_high)
-        per_token_loss1 = coef_1 * advantages.unsqueeze(1)
+
+        if self.args.delta is not None:
+            # Use clamp instead of min to handle tensor-float comparison
+            per_token_loss1 = torch.clamp(coef_1, max=self.args.delta) * advantages.unsqueeze(1)
+        else:
+            # Original GRPO clipping (only lower bound implicitly applied by the final min)
+            per_token_loss1 = coef_1 * advantages.unsqueeze(1)
+
         per_token_loss2 = coef_2 * advantages.unsqueeze(1)
         per_token_loss = -torch.min(per_token_loss1, per_token_loss2)
         if self.beta != 0.0: