Move PRMTrainer to trl.experimental.prm

docs/source/_toctree.yml

@@ -64,8 +64,6 @@
       title: GRPO
     - local: kto_trainer
       title: KTO
-    - local: prm_trainer
-      title: PRM
     - local: reward_trainer
       title: Reward
     - local: rloo_trainer
@@ -119,6 +117,8 @@
     title: PAPO
   - local: ppo_trainer
     title: PPO
+  - local: prm_trainer
+    title: PRM
   - local: xpo_trainer
     title: XPO
   - local: openenv

docs/source/dataset_formats.md

@@ -391,7 +391,6 @@ Choosing the right dataset type depends on the task you are working on and the s
 | [`GRPOTrainer`] | [Prompt-only](#prompt-only) |
 | [`KTOTrainer`] | [Unpaired preference](#unpaired-preference) or [Preference (explicit prompt recommended)](#preference) |
 | [`OnlineDPOTrainer`] | [Prompt-only](#prompt-only) |
-| [`PRMTrainer`] | [Stepwise supervision](#stepwise-supervision) |
 | [`RewardTrainer`] | [Preference (implicit prompt recommended)](#preference) |
 | [`RLOOTrainer`] | [Prompt-only](#prompt-only) |
 | [`SFTTrainer`] | [Language modeling](#language-modeling) or [Prompt-completion](#prompt-completion) |
@@ -401,6 +400,7 @@ Choosing the right dataset type depends on the task you are working on and the s
 | [`experimental.nash_md.NashMDTrainer`] | [Prompt-only](#prompt-only) |
 | [`experimental.orpo.ORPOTrainer`] | [Preference (explicit prompt recommended)](#preference) |
 | [`experimental.ppo.PPOTrainer`] | Tokenized language modeling |
+| [`experimental.prm.PRMTrainer`] | [Stepwise supervision](#stepwise-supervision) |
 | [`experimental.xpo.XPOTrainer`] | [Prompt-only](#prompt-only) |
 
 ## Using any dataset with TRL: preprocessing and conversion

docs/source/example_overview.md

@@ -62,7 +62,7 @@ Scripts are maintained in the [`trl/scripts`](https://github.com/huggingface/trl
 | [`examples/scripts/orpo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/orpo.py) | This script shows how to use the [`experimental.orpo.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 [`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/prm.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/prm.py) | This script shows how to use the [`experimental.prm.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. |
 | [`examples/scripts/sft.py`](https://github.com/huggingface/trl/blob/main/trl/scripts/sft.py) | This script shows how to use the [`SFTTrainer`] to fine-tune a model. |

docs/source/index.md

@@ -31,8 +31,8 @@ Below is the current list of TRL trainers, organized by method type (⚡️ = vL
 
 ### Reward modeling
 
-- [`PRMTrainer`]
 - [`RewardTrainer`]
+- [`experimental.prm.PRMTrainer`] 🧪
 
 </div>
 <div style="flex: 1; min-width: 0;">

docs/source/prm_trainer.md

@@ -2,6 +2,9 @@
 
 [![model badge](https://img.shields.io/badge/All_models-PRM-blue)](https://huggingface.co/models?other=prm,trl)
 
+> [!TIP]
+> PRMTrainer has been moved to `trl.experimental.prm.PRMTrainer`. The `trl.trainer` version is deprecated and will be removed in TRL 0.29.0. Please update your imports to use `trl.experimental.prm.PRMTrainer` instead. See [issue #4467](https://github.com/huggingface/trl/issues/4467) for more information.
+
 > [!WARNING]
 > PRM Trainer is an experimental API which is subject to change at any time.
 
@@ -31,7 +34,7 @@ Below is the script to train the model:
 ```python
 # train_prm.py
 from datasets import load_dataset
-from trl import PRMConfig, PRMTrainer
+from trl.experimental.prm import PRMConfig, PRMTrainer
 from transformers import AutoModelForTokenClassification, AutoTokenizer
 
 model = AutoModelForTokenClassification.from_pretrained("Qwen/Qwen2-0.5B", num_labels=2)
@@ -112,11 +115,11 @@ accelerate launch examples/scripts/prm.py \
 
 ## PRMTrainer
 
-[[autodoc]] PRMTrainer
+[[autodoc]] experimental.prm.PRMTrainer
     - train
     - save_model
     - push_to_hub
 
 ## PRMConfig
 
-[[autodoc]] PRMConfig
+[[autodoc]] experimental.prm.PRMConfig

examples/scripts/prm.py

@@ -58,13 +58,12 @@
 
 from trl import (
     ModelConfig,
-    PRMConfig,
-    PRMTrainer,
     ScriptArguments,
     get_kbit_device_map,
     get_peft_config,
     get_quantization_config,
 )
+from trl.experimental.prm import PRMConfig, PRMTrainer
 
 
 logger = logging.get_logger(__name__)

tests/test_prm_trainer.py → tests/experimental/test_prm_trainer.py

@@ -20,9 +20,9 @@
 from transformers import AutoModelForTokenClassification, AutoTokenizer, PreTrainedTokenizerBase
 from transformers.utils import is_peft_available
 
-from trl import PRMConfig, PRMTrainer
+from trl.experimental.prm import PRMConfig, PRMTrainer
 
-from .testing_utils import TrlTestCase, require_peft
+from ..testing_utils import TrlTestCase, require_peft
 
 
 if is_peft_available():

trl/experimental/prm/__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 .prm_config import PRMConfig
+from .prm_trainer import PRMTrainer
+
+
+__all__ = ["PRMConfig", "PRMTrainer"]

trl/experimental/prm/prm_config.py

@@ -0,0 +1,112 @@
+# 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, field
+
+from transformers import TrainingArguments
+
+
+@dataclass
+class PRMConfig(TrainingArguments):
+    r"""
+    Configuration class for the [`experimental.prm.PRMTrainer`].
+
+    This class includes only the parameters that are specific to PRM training. For a full list of training arguments,
+    please refer to the [`~transformers.TrainingArguments`] 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:
+        max_length (`int` or `None`, *optional*, defaults to `1024`):
+            Maximum length of the sequences (prompt + completion) used for truncation.
+        max_prompt_length (`int` or `None`, *optional*, defaults to `512`):
+            Maximum length of the prompt used for truncation.
+        max_completion_length (`int`, *optional*):
+            Maximum length of the completion used for truncation. The completion is the concatenation of the steps.
+        disable_dropout (`bool`, *optional*, defaults to `True`):
+            Whether to disable dropout in the model.
+        step_separator (`str`, *optional*, defaults to `"\n"`):
+            Separator used to separate each step of the reasoning process.
+        train_on_last_step_only (`bool`, *optional*, defaults to `False`):
+            Whether to train only on the last step.
+        dataset_num_proc (`int`, *optional*):
+            Number of processes to use for processing the dataset.
+    """
+
+    # Parameters whose default values are overridden from TrainingArguments
+    learning_rate: float = field(
+        default=1e-5,
+        metadata={"help": "The initial learning rate for AdamW."},
+    )
+    logging_steps: float = field(
+        default=10,
+        metadata={
+            "help": "Log every X updates steps. Should be an integer or a float in range `[0,1)`. If smaller than 1, "
+            "will be interpreted as ratio of total training steps."
+        },
+    )
+    gradient_checkpointing: bool = field(
+        default=True,
+        metadata={
+            "help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
+        },
+    )
+    bf16: bool | None = field(
+        default=None,
+        metadata={
+            "help": "Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA "
+            "architecture or Intel XPU or using CPU (use_cpu) or Ascend NPU. If not set, it defaults to `True` if "
+            "`fp16` is not set."
+        },
+    )
+
+    max_length: int | None = field(
+        default=1024,
+        metadata={"help": "Maximum length of the sequences (prompt + completion) used for truncation."},
+    )
+    max_prompt_length: int | None = field(
+        default=512,
+        metadata={"help": "Maximum length of the prompt used for truncation."},
+    )
+    max_completion_length: int | None = field(
+        default=None,
+        metadata={
+            "help": "Maximum length of the completion used for truncation. The completion is the concatenation of the "
+            "steps."
+        },
+    )
+    disable_dropout: bool = field(
+        default=True,
+        metadata={"help": "Whether to disable dropout in the model and reference model."},
+    )
+    step_separator: str = field(
+        default="\n",
+        metadata={"help": "Separator used to separate each step of the reasoning process."},
+    )
+    train_on_last_step_only: bool = field(
+        default=False,
+        metadata={"help": "Whether to train only on the last step."},
+    )
+    dataset_num_proc: int | None = field(
+        default=None,
+        metadata={"help": "Number of processes to use for processing the dataset."},
+    )
+
+    def __post_init__(self):
+        self.bf16 = not (self.fp16) if self.bf16 is None else self.bf16
+
+        super().__post_init__()