🎬 Clip higher

trl/trainer/grpo_config.py

@@ -115,6 +115,9 @@ 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.
+        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`.
         reward_weights (`list[float]` or `None`, *optional*, defaults to `None`):
             Weights for each reward function. Must match the number of reward functions. If `None`, all rewards are
             weighted equally with weight `1.0`.
@@ -300,6 +303,13 @@ class GRPOConfig(TrainingArguments):
         default=0.2,
         metadata={"help": "Epsilon value for clipping."},
     )
+    epsilon_high: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": "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 recommends `0.28`."
+        },
+    )
     reward_weights: Optional[list[float]] = field(
         default=None,
         metadata={

trl/trainer/grpo_trainer.py

@@ -388,7 +388,8 @@ def data_collator(features):  # No data collation is needed in GRPO
 
         # Multi-step
         self.num_iterations = args.num_iterations  # = 𝜇 in the GRPO paper
-        self.epsilon = args.epsilon
+        self.epsilon_low = args.epsilon
+        self.epsilon_high = args.epsilon_high if args.epsilon_high is not None else args.epsilon
         # Tracks the number of iterations (forward + backward passes), including those within a gradient accumulation cycle.
         self._step = 0
         # Buffer the batch to reuse generated outputs across multiple updates. For more details, see
@@ -975,7 +976,7 @@ def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=N
         # _generate_and_score_completions) and use per_token_logps.detach() instead.
         old_per_token_logps = inputs["old_per_token_logps"] if self.num_iterations > 1 else per_token_logps.detach()
         coef_1 = torch.exp(per_token_logps - old_per_token_logps)
-        coef_2 = torch.clamp(coef_1, 1 - self.epsilon, 1 + self.epsilon)
+        coef_2 = torch.clamp(coef_1, 1 - self.epsilon_low, 1 + self.epsilon_high)
         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)