Khalil Meftah 8a3d64033f Reward models refactor (#3142) ...

* feat(rewards): add RewardModelConfig and PreTrainedRewardModel base classes

* refactor(rewards): migrate Classifier from policies/sac/reward_model/ to rewards/classifier/

* refactor(rewards): migrate SARM from policies/sarm/ to rewards/sarm/

* refactor(rewards): add rewards/factory.py and remove reward model code from policies/factory.py

* refactor(rewards): update imports and delete old reward model locations

* test(rewards): add reward model tests and update existing test imports

* fix(rewards): restore full Classifier and SARM implementations

* test(rewards): restore missing CUDA and mixed precision classifier processor tests

* refactor(lerobot_train.py): remove rabc specific configuration and replace it with a generic samplerweight class in lerobot_train

* refactor(lerobot_train.py): add missing sampling weight script

* linter + missing files

* add testing for sampl weighter

* revert some useless changes, improve typing

* update docs

* add automatic detection of the progress path

* remove type exp

* improve comment

* fix: move rabc.py to rewards/sarm/ and update import paths

* refactor(imports): update reward model imports to new module structure

* refactor(imports): update reward model imports to reflect new module structure

* refactor(imports): conditionally import pandas based on availability

* feat(configs): add reward_model field to TrainPipelineConfig and Hub fields to RewardModelConfig

* refactor(policies): remove reward model branches from policy factory and __init__

* refactor(rewards): expand __init__ facade and fix SARMConfig __post_init__ crash

* feat(train): route reward model training through rewards/factory instead of policies/factory

* refactor(train): streamline reward model training logic

* fix(rewards): ensure FileNotFoundError is raised for missing config_file

* refactor(train): update __get_path_fields__ to include reward_model for config loading

* refactor(classifier): remove redundant input normalization in predict_reward method

* fix(train): raise ValueError for non-trainable reward models in train function

* refactor(pretrained_rm): add model card template

* refactor(tests): reward models

* refactor(sarm): update reset method and remove unused action prediction methods

* refactor(wandb): differentiate tags for reward model and policy training in cfg_to_group function

* fix(train): raise ValueError for PEFT usage in reward model training

* refactor(rewards): enhance RewardModelConfig with device handling and delta indices properties

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Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>

2026-04-28 17:56:24 +02:00

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