Add entropy regularization to GRPO

docs/source/grpo_trainer.md

@@ -185,7 +185,10 @@ While training and evaluating, we record the following reward metrics:
 - `reward`: The overall average reward after summing rewards across functions (weighted by `reward_weights`).
 - `reward_std`: The standard deviation of summed rewards across functions (weighted by `reward_weights`), computed over the full batch.
 - `frac_reward_zero_std`: The fraction of samples in the generation batch with a reward std of zero, implying there is little diversity for that prompt (all answers are correct or incorrect).
+- `policy_loss`: The policy gradient loss value (before any entropy bonus).
 - `entropy`: Average entropy of token predictions across generated completions. (If `mask_truncated_completions=True`, masked sequences tokens are excluded.)
+- `entropy_loss`: Mean per-token entropy (nats) used as the regularization signal. Logged when `entropy_coef` is nonzero or `use_adaptive_entropy=True`.
+- `entropy_coef`: The current entropy coefficient. Logged when `entropy_coef` is nonzero or `use_adaptive_entropy=True`. Updated once per optimizer step when `use_adaptive_entropy=True`.
 - `kl`: The average KL divergence between the model and the reference model, calculated over generated completions. Logged only if `beta` is nonzero.
 - `clip_ratio/region_mean`: The ratio of token (or sequence, if `importance_sampling_level="sequence"`) probabilities where the GRPO objective is clipped to stay within the trust region:  \\( \text{clip}\left( r_{i,t}(\theta), 1 - \epsilon_\mathrm{low}, 1 + \epsilon_\mathrm{high} \right)\,, \quad r_{i,t}(\theta) = \frac{\pi_\theta(o_{i,t} \mid q, o_{i,< t})}{\pi_{\theta_{\text{old}}}(o_{i,t} \mid q, o_{i,< t})} \\). A higher value means more tokens are clipped, which constrains how much the policy $\pi_\theta$ can change.
 - `clip_ratio/low_mean`: The average ratio of token (or sequence, if `importance_sampling_level="sequence"`) probabilities that were clipped on the lower bound of the trust region:  \\(r_{i,t}(\theta) < 1 - \epsilon_\mathrm{low}\\).
@@ -641,6 +644,46 @@ and the reward will be computed as the sum of the rewards from each function, or
 
 Note that [`GRPOTrainer`] supports multiple reward functions of different types. See the parameters documentation for more details.
 
+### Entropy regularization
+
+To encourage exploration and prevent the policy from collapsing to near-deterministic outputs, you can add an entropy bonus to the training objective. The entropy regularization augments the GRPO loss as follows:
+
+$$
+\mathcal{L}(\theta) = \mathcal{L}_{\text{GRPO}}(\theta) - \alpha \cdot \mathcal{H}(\pi_\theta),
+$$
+
+where \\(\mathcal{H}(\pi_\theta)\\) is the mean per-token entropy of the policy and \\(\alpha\\) is the entropy coefficient.
+
+**Static entropy** — a fixed coefficient throughout training:
+
+```python
+from trl import GRPOConfig, GRPOTrainer
+
+training_args = GRPOConfig(entropy_coef=0.05, ...)
+```
+
+**Adaptive entropy** — the coefficient is updated each optimizer step based on a target entropy, as introduced in [Skywork-OR1](https://huggingface.co/papers/2505.22312). When the current entropy falls at or below `entropy_target`, the coefficient is incremented by `entropy_coef_delta`; otherwise it is decremented. The coefficient is only applied (i.e. non-zero) while entropy is at or below the target:
+
+```python
+training_args = GRPOConfig(
+    entropy_coef=0.01,          # initial coefficient
+    use_adaptive_entropy=True,
+    entropy_target=5.0,         # target mean per-token entropy (nats); tune for your model
+    entropy_coef_delta=0.005,   # step size per optimizer step
+    entropy_coef_min=0.0,
+    entropy_coef_max=1.0,
+    ...
+)
+```
+
+<Tip>
+
+Typical language models have per-token entropies of 2–10 nats. The default `entropy_target=0.2` nearly always triggers regularization; set it to a value meaningful for your model (e.g. the entropy you observe early in training, logged as the `entropy` metric).
+
+</Tip>
+
+When `use_adaptive_entropy=True`, the current entropy coefficient `entropy_coef` is saved alongside each checkpoint and restored on resume, so training is fully resumable.
+
 ### Rapid Experimentation for GRPO
 
 RapidFire AI is an open-source experimentation engine that sits on top of TRL and lets you launch multiple GRPO configurations at once, even on a single GPU. Instead of trying configurations sequentially, RapidFire lets you **see all their learning curves earlier, stop underperforming runs, and clone promising ones with new settings in flight** without restarting. For more information, see [RapidFire AI Integration](rapidfire_integration).

docs/source/paper_index.md

@@ -225,6 +225,27 @@ training_args = GRPOConfig(
 )
 ```
 
+### Skywork-OR1: Open Reasoning Models
+
+**📜 Paper**: https://huggingface.co/papers/2505.22312
+
+Skywork-OR1 is a family of open reasoning models trained with GRPO. The paper introduces **adaptive entropy control**: an entropy regularization term `−α·H(π_θ)` is added to the GRPO objective, and the coefficient `α` is automatically adjusted each optimizer step. When the model's mean per-token entropy falls at or below a target, `α` is incremented to encourage more exploration; otherwise it is decremented. The bonus is only applied while entropy is at or below the target. To replicate this adaptive entropy control, use the following configuration:
+
+```python
+from trl import GRPOConfig, GRPOTrainer
+
+training_args = GRPOConfig(
+    use_adaptive_entropy=True,   # enable adaptive entropy control (Section 3.3 of the paper)
+    entropy_coef=0.01,           # initial entropy regularization coefficient
+    entropy_target=5.0,          # target mean per-token entropy (nats); tune for your model
+    entropy_coef_delta=0.005,    # step size for coefficient updates per optimizer step
+)
+trainer = GRPOTrainer(
+    ...,
+    args=training_args,
+)
+```
+
 ### Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
 
 **📜 Paper**: https://huggingface.co/papers/2506.01939

tests/test_grpo_trainer.py

@@ -1474,6 +1474,74 @@ def test_train_with_cast_lm_head_to_fp32(self, model_name):
             new_param = trainer.model.get_parameter(n)
             assert not torch.equal(param, new_param), f"Parameter {n} has not changed."
 
+    def test_train_with_static_entropy(self):
+        dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
+        training_args = GRPOConfig(
+            output_dir=self.tmp_dir,
+            learning_rate=0.1,  # use higher lr because gradients are tiny and default lr can stall updates
+            per_device_train_batch_size=3,  # reduce the batch size to reduce memory usage
+            num_generations=3,  # reduce the number of generations to reduce memory usage
+            max_completion_length=8,  # reduce the completion length to reduce memory usage
+            report_to="none",
+            entropy_coef=0.1,
+        )
+        trainer = GRPOTrainer(
+            model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
+            reward_funcs="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
+            args=training_args,
+            train_dataset=dataset,
+        )
+
+        previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
+
+        trainer.train()
+
+        assert trainer.state.log_history[-1]["train_loss"] is not None
+        assert trainer.state.log_history[-1]["policy_loss"] is not None
+        assert trainer.state.log_history[-1]["entropy_loss"] is not None
+        assert trainer.state.log_history[-1]["entropy_coef"] is not None
+
+        # Check that the params have changed
+        for n, param in previous_trainable_params.items():
+            new_param = trainer.model.get_parameter(n)
+            assert not torch.equal(param, new_param), f"Parameter {n} has not changed."
+
+    def test_train_with_adaptive_entropy(self):
+        dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
+        training_args = GRPOConfig(
+            output_dir=self.tmp_dir,
+            learning_rate=0.1,  # use higher lr because gradients are tiny and default lr can stall updates
+            per_device_train_batch_size=3,  # reduce the batch size to reduce memory usage
+            num_generations=3,  # reduce the number of generations to reduce memory usage
+            max_completion_length=8,  # reduce the completion length to reduce memory usage
+            report_to="none",
+            entropy_coef=0.01,
+            use_adaptive_entropy=True,
+            entropy_target=15.0,  # above any realistic entropy → coef is always incremented
+        )
+        trainer = GRPOTrainer(
+            model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
+            reward_funcs="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5",
+            args=training_args,
+            train_dataset=dataset,
+        )
+
+        previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
+
+        trainer.train()
+
+        assert trainer.state.log_history[-1]["train_loss"] is not None
+        assert trainer.state.log_history[-1]["policy_loss"] is not None
+        assert trainer.state.log_history[-1]["entropy_loss"] is not None
+        assert trainer.state.log_history[-1]["entropy_coef"] is not None
+        # Coefficient should have increased since entropy < target throughout training
+        assert trainer.entropy_coef > 0.01
+
+        # Check that the params have changed
+        for n, param in previous_trainable_params.items():
+            new_param = trainer.model.get_parameter(n)
+            assert not torch.equal(param, new_param), f"Parameter {n} has not changed."
+
     def test_train_with_entropy_filter(self):
         dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only", split="train")
         training_args = GRPOConfig(

trl/trainer/grpo_config.py

@@ -295,6 +295,28 @@ class GRPOConfig(_BaseConfig):
             position, improving results. Range: `[0.0-1.0]`. A value of `0.0` masks all but the highest entropy token;
             `1.0` keeps all tokens. The paper recommends a value of `0.2`. If used with
             `mask_truncated_completions=True`, only tokens from non-truncated completions are considered.
+        entropy_coef (`float`, *optional*, defaults to `0.0`):
+            Coefficient of the entropy regularization term in the loss. A positive value adds an entropy bonus that
+            encourages exploration by keeping the policy from collapsing to near-deterministic outputs. When
+            `use_adaptive_entropy=True`, this serves as the initial coefficient and is updated each optimizer step.
+            Has no effect when set to `0.0` (default).
+        use_adaptive_entropy (`bool`, *optional*, defaults to `False`):
+            Whether to use adaptive entropy control, introduced in
+            [Skywork-OR1](https://huggingface.co/papers/2505.22312). When enabled, the entropy coefficient
+            `entropy_coef` is updated each optimizer step: incremented by `entropy_coef_delta` when the current
+            entropy is below `entropy_target`, and decremented otherwise. The coefficient is only applied when
+            entropy is at or below `entropy_target`.
+        entropy_coef_min (`float`, *optional*, defaults to `0.0`):
+            Lower bound for the entropy coefficient when using adaptive entropy control.
+        entropy_coef_max (`float`, *optional*, defaults to `1.0`):
+            Upper bound for the entropy coefficient when using adaptive entropy control.
+        entropy_coef_delta (`float`, *optional*, defaults to `0.005`):
+            Step size for adjusting the entropy coefficient at each optimizer step during adaptive entropy control.
+        entropy_target (`float`, *optional*, defaults to `0.2`):
+            Target mean per-token entropy (in nats) used by adaptive entropy control. The coefficient is only
+            applied when the current entropy falls at or below this value. Typical language models have per-token
+            entropies in the range 2–10 nats; the default of `0.2` nearly always triggers regularization, so users
+            should tune this to a value appropriate for their model and task.
         max_tool_calling_iterations (`int`, *optional*):
             Maximum number of tool-calling turns when training an agent. If `None`, there is no limit and generation
             stops when the model generates a response turn with no tool calls or when the total response length reaches
@@ -832,6 +854,42 @@ class GRPOConfig(_BaseConfig):
             "non-truncated completions are considered."
         },
     )
+    entropy_coef: float = field(
+        default=0.0,
+        metadata={
+            "help": "Coefficient of the entropy regularization term in the loss. A positive value adds an entropy "
+            "bonus that encourages exploration. When `use_adaptive_entropy=True`, this serves as the initial "
+            "coefficient and is updated each optimizer step. Has no effect when set to `0.0` (default)."
+        },
+    )
+    use_adaptive_entropy: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to use adaptive entropy control, introduced in Skywork-OR1 "
+            "(https://huggingface.co/papers/2505.22312). When enabled, `entropy_coef` is incremented by "
+            "`entropy_coef_delta` when entropy is below `entropy_target`, and decremented otherwise."
+        },
+    )
+    entropy_coef_min: float = field(
+        default=0.0,
+        metadata={"help": "Lower bound for the entropy coefficient when using adaptive entropy control."},
+    )
+    entropy_coef_max: float = field(
+        default=1.0,
+        metadata={"help": "Upper bound for the entropy coefficient when using adaptive entropy control."},
+    )
+    entropy_coef_delta: float = field(
+        default=0.005,
+        metadata={"help": "Step size for adjusting the entropy coefficient during adaptive entropy control."},
+    )
+    entropy_target: float = field(
+        default=0.2,
+        metadata={
+            "help": "Target mean per-token entropy (nats) for adaptive entropy control. The coefficient is only "
+            "applied when current entropy is at or below this value. Typical language models have per-token "
+            "entropies of 2–10 nats; the default of 0.2 nearly always triggers regularization, so tune this."
+        },
+    )
     max_tool_calling_iterations: int | None = field(
         default=None,
         metadata={