Move PPOTrainer to trl.experimental.ppo

README.md

@@ -25,7 +25,7 @@ Explore how to seamlessly integrate TRL with OpenEnv in our [dedicated documenta
 
 ## Overview
 
-TRL is a cutting-edge library designed for post-training foundation models using advanced techniques like Supervised Fine-Tuning (SFT), Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO). Built on top of the [🤗 Transformers](https://github.com/huggingface/transformers) ecosystem, TRL supports a variety of model architectures and modalities, and can be scaled-up across various hardware setups.
+TRL is a cutting-edge library designed for post-training foundation models using advanced techniques like Supervised Fine-Tuning (SFT), Group Realtive Policy Optimization (GRPO), and Direct Preference Optimization (DPO). Built on top of the [🤗 Transformers](https://github.com/huggingface/transformers) ecosystem, TRL supports a variety of model architectures and modalities, and can be scaled-up across various hardware setups.
 
 ## Highlights
 

docs/source/_toctree.yml

@@ -66,8 +66,6 @@
       title: KTO
     - local: orpo_trainer
       title: ORPO
-    - local: ppo_trainer
-      title: PPO
     - local: prm_trainer
       title: PRM
     - local: reward_trainer
@@ -119,6 +117,8 @@
     title: Nash-MD
   - local: papo_trainer
     title: PAPO
+  - local: ppo_trainer
+    title: PPO
   - local: xpo_trainer
     title: XPO
   - local: openenv

docs/source/dataset_formats.md

@@ -396,7 +396,7 @@ Choosing the right dataset type depends on the task you are working on and the s
 | [`experimental.nash_md.NashMDTrainer`] | [Prompt-only](#prompt-only) |
 | [`OnlineDPOTrainer`] | [Prompt-only](#prompt-only) |
 | [`ORPOTrainer`] | [Preference (explicit prompt recommended)](#preference) |
-| [`PPOTrainer`] | Tokenized language modeling |
+| [`experimental.ppo.PPOTrainer`] | Tokenized language modeling |
 | [`PRMTrainer`] | [Stepwise supervision](#stepwise-supervision) |
 | [`RewardTrainer`] | [Preference (implicit prompt recommended)](#preference) |
 | [`RLOOTrainer`] | [Prompt-only](#prompt-only) |

docs/source/example_overview.md

@@ -37,7 +37,7 @@ These notebooks are easier to run and are designed for quick experimentation wit
 
 Scripts are maintained in the [`trl/scripts`](https://github.com/huggingface/trl/blob/main/trl/scripts) and [`examples/scripts`](https://github.com/huggingface/trl/blob/main/examples/scripts) directories. They show how to use different trainers such as `SFTTrainer`, `PPOTrainer`, `DPOTrainer`, `GRPOTrainer`, and more.
 
- File | Description |
+| File | Description |
 | --- | --- |
 | [`examples/scripts/bco.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/bco.py) | This script shows how to use the [`KTOTrainer`] with the BCO loss to fine-tune a model to increase instruction-following, truthfulness, honesty, and helpfulness using the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset. |
 | [`examples/scripts/cpo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/cpo.py) | This script shows how to use the [`experimental.cpo.CPOTrainer`] to fine-tune a model to increase helpfulness and harmlessness using the [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) dataset. |
@@ -55,8 +55,8 @@ Scripts are maintained in the [`trl/scripts`](https://github.com/huggingface/trl
 | [`examples/scripts/online_dpo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/online_dpo.py) | This script shows how to use the [`OnlineDPOTrainer`] to fine-tune a model. |
 | [`examples/scripts/online_dpo_vlm.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/online_dpo_vlm.py) | This script shows how to use the [`OnlineDPOTrainer`] to fine-tune a a Vision Language Model. |
 | [`examples/scripts/orpo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) | This script shows how to use the [`ORPOTrainer`] to fine-tune a model to increase helpfulness and harmlessness using the [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) dataset. |
-| [`examples/scripts/ppo/ppo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/ppo/ppo.py) | This script shows how to use the [`PPOTrainer`] to fine-tune a model to improve its ability to continue text with positive sentiment or physically descriptive language. |
-| [`examples/scripts/ppo/ppo_tldr.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/ppo/ppo_tldr.py) | This script shows how to use the [`PPOTrainer`] to fine-tune a model to improve its ability to generate TL;DR summaries. |
+| [`examples/scripts/ppo/ppo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/ppo/ppo.py) | This script shows how to use the [`experimental.ppo.PPOTrainer`] to fine-tune a model to improve its ability to continue text with positive sentiment or physically descriptive language. |
+| [`examples/scripts/ppo/ppo_tldr.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/ppo/ppo_tldr.py) | This script shows how to use the [`experimental.ppo.PPOTrainer`] to fine-tune a model to improve its ability to generate TL;DR summaries. |
 | [`examples/scripts/prm.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/prm.py) | This script shows how to use the [`PRMTrainer`] to fine-tune a Process-supervised Reward Model (PRM). |
 | [`examples/scripts/reward_modeling.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/reward_modeling.py) | This script shows how to use the [`RewardTrainer`] to train an Outcome Reward Model (ORM) on your own dataset. |
 | [`examples/scripts/rloo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/rloo.py) | This script shows how to use the [`RLOOTrainer`] to fine-tune a model to improve its ability to solve math questions. |

docs/source/index.md

@@ -25,8 +25,8 @@ Below is the current list of TRL trainers, organized by method type (⚡️ = vL
 - [`GRPOTrainer`] ⚡️
 - [`RLOOTrainer`] ⚡️
 - [`OnlineDPOTrainer`] ⚡️
-- [`PPOTrainer`]
 - [`experimental.nash_md.NashMDTrainer`] 🧪 ⚡️
+- [`experimental.ppo.PPOTrainer`] 🧪
 - [`experimental.xpo.XPOTrainer`] 🧪 ⚡️
 
 ### Reward modeling

docs/source/peft_integration.md

@@ -146,7 +146,8 @@ After training your reward adapter and pushing it to the Hub:
 
 ```python
 from peft import LoraConfig
-from trl import AutoModelForCausalLMWithValueHead, PPOTrainer
+from trl import AutoModelForCausalLMWithValueHead
+from trl.experimental.ppo import PPOTrainer
 
 model_name = "huggyllama/llama-7b"
 rm_adapter_id = "trl-lib/llama-7b-hh-rm-adapter"

docs/source/ppo_trainer.md

@@ -1,5 +1,11 @@
 # PPO Trainer
 
+<Tip warning={true}>
+
+**Deprecation Notice**: PPOTrainer and PPOConfig have been moved to `trl.experimental.ppo` and will be removed from `trl.trainer` in TRL 0.29.0. Please update your imports to use `from trl.experimental.ppo import PPOConfig, PPOTrainer` instead. See [issue #4466](https://github.com/huggingface/trl/issues/4466) for more information.
+
+</Tip>
+
 [![model badge](https://img.shields.io/badge/All_models-PPO-blue)](https://huggingface.co/models?other=ppo,trl)
 
 TRL supports training LLMs with [Proximal Policy Optimization (PPO)](https://huggingface.co/papers/1707.06347).
@@ -228,11 +234,11 @@ python -m openrlbenchmark.rlops_multi_metrics \
 
 ## PPOTrainer
 
-[[autodoc]] PPOTrainer
+[[autodoc]] experimental.ppo.PPOTrainer
     - train
     - save_model
     - push_to_hub
 
 ## PPOConfig
 
-[[autodoc]] PPOConfig
+[[autodoc]] experimental.ppo.PPOConfig

docs/source/reducing_memory_usage.md

@@ -274,7 +274,7 @@ training_args = OnlineDPOConfig(..., ds3_gather_for_generation=False)
 <hfoption id="PPO">
 
 ```python
-from trl import PPOConfig
+from trl.experimental.ppo import PPOConfig
 
 training_args = PPOConfig(..., ds3_gather_for_generation=False)
 ```

examples/scripts/ppo/ppo.py

@@ -34,15 +34,8 @@
     HfArgumentParser,
 )
 
-from trl import (
-    ModelConfig,
-    PPOConfig,
-    PPOTrainer,
-    ScriptArguments,
-    get_kbit_device_map,
-    get_peft_config,
-    get_quantization_config,
-)
+from trl import ModelConfig, ScriptArguments, get_kbit_device_map, get_peft_config, get_quantization_config
+from trl.experimental.ppo import PPOConfig, PPOTrainer
 
 
 # Enable logging in a Hugging Face Space

examples/scripts/ppo/ppo_tldr.py

@@ -34,15 +34,8 @@
     HfArgumentParser,
 )
 
-from trl import (
-    ModelConfig,
-    PPOConfig,
-    PPOTrainer,
-    ScriptArguments,
-    get_kbit_device_map,
-    get_peft_config,
-    get_quantization_config,
-)
+from trl import ModelConfig, ScriptArguments, get_kbit_device_map, get_peft_config, get_quantization_config
+from trl.experimental.ppo import PPOConfig, PPOTrainer
 
 
 # Enable logging in a Hugging Face Space

tests/test_ppo_trainer.py → tests/experimental/test_ppo_trainer.py

@@ -17,10 +17,10 @@
 from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
 from transformers.utils import is_peft_available
 
-from trl import PPOConfig, PPOTrainer
-from trl.trainer.ppo_trainer import masked_mean, masked_var, masked_whiten
+from trl.experimental.ppo import PPOConfig, PPOTrainer
+from trl.experimental.ppo.ppo_trainer import masked_mean, masked_var, masked_whiten
 
-from .testing_utils import TrlTestCase, require_peft
+from ..testing_utils import TrlTestCase, require_peft
 
 
 if is_peft_available():

trl/experimental/ppo/__init__.py

@@ -0,0 +1,19 @@
+# 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 .ppo_config import PPOConfig
+from .ppo_trainer import PPOTrainer
+
+
+__all__ = ["PPOConfig", "PPOTrainer"]

trl/experimental/ppo/ppo_config.py

@@ -0,0 +1,135 @@
+# 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.
+
+import os
+from dataclasses import dataclass, field
+from typing import Literal
+
+from ...trainer.utils import OnPolicyConfig
+
+
+@dataclass
+class PPOConfig(OnPolicyConfig):
+    r"""
+    Configuration class for the [`experimental.ppo.PPOTrainer`].
+
+    This class includes only the parameters that are specific to PPO training. For a full list of training arguments,
+    please refer to the [`~transformers.TrainingArguments`] and [`OnPolicyConfig`] documentation. Note that default
+    values in this class may differ from those in [`~transformers.TrainingArguments`].
+
+    Using [`~transformers.HfArgumentParser`] we can turn this class into
+    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
+    command line.
+
+    Parameters:
+        exp_name (`str`, *optional*, defaults to `os.path.basename(__file__)[:-3]`):
+            Name of this experiment.
+        reward_model_path (`str`, *optional*, defaults to `"EleutherAI/pythia-160m"`):
+            Path to the reward model.
+        model_adapter_name (`str`, *optional*):
+            Name of the train target PEFT adapter, when using LoRA with multiple adapters.
+        ref_adapter_name (`str`, *optional*):
+            Name of the reference PEFT adapter, when using LoRA with multiple adapters.
+        num_ppo_epochs (`int`, *optional*, defaults to `4`):
+            Number of epochs to train.
+        whiten_rewards (`bool`, *optional*, defaults to `False`):
+            Whether to whiten the rewards.
+        kl_coef (`float`, *optional*, defaults to `0.05`):
+            KL coefficient.
+        kl_estimator (`Literal["k1", "k3"]`, *optional*, defaults to `"k1"`):
+            Which estimator for KL-Divergence to use from [Approximating KL
+            Divergence](http://joschu.net/blog/kl-approx.html). Defaults to "k1", a straightforward, unbiased
+            estimator. Can be set to "k3", an unbiased estimator with lower variance which "appears to be a strictly
+            better estimator". Cannot be set to "k2", as it is used for logging purposes.
+        cliprange (`float`, *optional*, defaults to `0.2`):
+            Clip range.
+        vf_coef (`float`, *optional*, defaults to `0.1`):
+            Value function coefficient.
+        cliprange_value (`float`, *optional*, defaults to `0.2`):
+            Clip range for the value function.
+        gamma (`float`, *optional*, defaults to `1.0`):
+            Discount factor.
+        lam (`float`, *optional*, defaults to `0.95`):
+            Lambda value for GAE.
+        ds3_gather_for_generation (`bool`, *optional*, defaults to `True`):
+            This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
+            improving generation speed. However, disabling this option allows training models that exceed the VRAM
+            capacity of a single GPU, albeit at the cost of slower generation.
+    """
+
+    exp_name: str = field(
+        default=os.path.basename(__file__)[:-3],
+        metadata={"help": "Name of this experiment."},
+    )
+    reward_model_path: str = field(
+        default="EleutherAI/pythia-160m",
+        metadata={"help": "Path to the reward model."},
+    )
+    model_adapter_name: str | None = field(
+        default=None,
+        metadata={"help": "Name of the train target PEFT adapter, when using LoRA with multiple adapters."},
+    )
+    ref_adapter_name: str | None = field(
+        default=None,
+        metadata={"help": "Name of the reference PEFT adapter, when using LoRA with multiple adapters."},
+    )
+    num_ppo_epochs: int = field(
+        default=4,
+        metadata={"help": "Number of epochs to train."},
+    )
+    whiten_rewards: bool = field(
+        default=False,
+        metadata={"help": "Whether to whiten the rewards."},
+    )
+    kl_coef: float = field(
+        default=0.05,
+        metadata={"help": "KL coefficient."},
+    )
+    kl_estimator: Literal["k1", "k3"] = field(
+        default="k1",
+        metadata={
+            "help": "Which estimator for KL-Divergence to use from Approximating KL Divergence "
+            "(http://joschu.net/blog/kl-approx.html). Defaults to 'k1', a straightforward, unbiased estimator. Can be "
+            "set to 'k3', an unbiased estimator with lower variance which 'appears to be a strictly better "
+            "estimator'. Cannot be set to 'k2', as it is used for logging purposes."
+        },
+    )
+    cliprange: float = field(
+        default=0.2,
+        metadata={"help": "Clip range."},
+    )
+    vf_coef: float = field(
+        default=0.1,
+        metadata={"help": "Value function coefficient."},
+    )
+    cliprange_value: float = field(
+        default=0.2,
+        metadata={"help": "Clip range for the value function."},
+    )
+    gamma: float = field(
+        default=1.0,
+        metadata={"help": "Discount factor."},
+    )
+    lam: float = field(
+        default=0.95,
+        metadata={"help": "Lambda value for GAE."},
+    )
+    ds3_gather_for_generation: bool = field(
+        default=True,
+        metadata={
+            "help": "This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for "
+            "generation, improving generation speed. However, disabling this option allows training models that "
+            "exceed the VRAM capacity of a single GPU, albeit at the cost of slower generation."
+        },
+    )