feat(trainer): add PAPOTrainer for preference-based optimization

docs/source/paper_index.md

@@ -207,6 +207,14 @@ training_args = GRPOConfig(
 
 See [Experimental - GFPO](experimental#gfpo).
 
+
+### PAPO (Preference Alignment via Policy Optimization)
+
+* **Paper:** [Link to Paper.](https://arxiv.org/abs/2507.06448)
+* **Implementation:**  `trl.experimental.papo_trainer.PAPOTrainer`
+* **Description:  A novel policy gradient algorithm that encourages VLMs to learn to perceive while learning to reason.**
+* **Note:  This is a TRL adoption, NOT PAPO's official code!** [Link to PAPO offical repo.](https://github.com/MikeWangWZHL/PAPO)
+
 ## Direct Policy Optimization
 
 Papers relating to the [`DPOTrainer`]

trl/experimental/papo/__init__.py

@@ -0,0 +1,17 @@
+# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from .papo_config import PAPOConfig
+from .papo_trainer import PAPOTrainer

trl/experimental/papo/papo_config.py

@@ -0,0 +1,78 @@
+# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import Literal
+
+from trl.trainer.grpo_config import GRPOConfig
+
+
+@dataclass
+class PAPOConfig(GRPOConfig):
+    """
+    Configuration class for PAPOTrainer.
+
+    PAPO (Perception-Aware Policy Optimization) extends GRPO/DAPO for multimodal reasoning
+    by adding an implicit perception loss and double entropy regularization.
+
+    Args:
+        perception_loss_weight (`float`, *optional*, defaults to `0.1`):
+            gamma
+            Weight coefficient for the perception loss term. This encourages the model to
+            be sensitive to visual changes.
+
+        mask_ratio (`float`, *optional*, defaults to `0.3`):
+            Ratio of the image to mask when computing perception loss.
+
+        mask_type (`Literal["random", "patch", "grid"]`, *optional*, defaults to `"random"`):
+            Type of masking strategy to use.
+
+        der_loss_weight1 (`float`, *optional*, defaults to `0.03`):
+            eta1
+            Weight coefficient for the Double Entropy Regularization (DER) term. This term
+            encourages confident predictions with original images (low entropy) and
+            uncertain predictions with masked images (high entropy).
+
+        der_loss_weight2 (`float`, *optional*, defaults to `0.03`):
+            eta2
+            Weight coefficient for the Double Entropy Regularization (DER) term. This term
+            encourages confident predictions with original images (low entropy) and
+            uncertain predictions with masked images (high entropy).
+
+        loss_type (`Literal["grpo", "dapo"]`, inherited from GRPOConfig):
+            Base loss type to use. Set to "grpo" for PAPO-G or "dapo" for PAPO-D.
+    """
+
+    perception_loss_weight: float = 0.1
+    mask_ratio: float = 0.3
+    mask_type: Literal["random", "patch", "grid"] = "random"
+
+    # Added for Double Entropy Regularization
+    der_loss_weight1: float = 0.03
+    der_loss_weight2: float = 0.03
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        # Validation
+        if not 0.0 <= self.mask_ratio <= 1.0:
+            raise ValueError(f"mask_ratio must be between 0 and 1, got {self.mask_ratio}")
+
+        if self.der_loss_weight1 < 0 or self.der_loss_weight2 < 0:
+            raise ValueError(
+                f"der_loss_weight1 and der_loss_weight2 must be non-negative, got {self.der_loss_weight1} and {self.der_loss_weight2}"
+            )
+
+        if self.mask_type not in ["random", "patch", "grid"]:
+            raise ValueError(f"mask_type must be one of ['random', 'patch', 'grid'], got {self.mask_type}")