| # Trainer | |
| At TRL we support PPO (Proximal Policy Optimisation) with an implementation that largely follows the structure introduced in the paper "Fine-Tuning Language Models from Human Preferences" by D. Ziegler et al. [[paper](https://huggingface.co./papers/1909.08593), [code](https://github.com/openai/lm-human-preferences)]. | |
| The Trainer and model classes are largely inspired from `transformers.Trainer` and `transformers.AutoModel` classes and adapted for RL. | |
| We also support a `RewardTrainer` that can be used to train a reward model. | |
| ## CPOConfig | |
| [[autodoc]] CPOConfig | |
| ## CPOTrainer | |
| [[autodoc]] CPOTrainer | |
| ## DDPOConfig | |
| [[autodoc]] DDPOConfig | |
| ## DDPOTrainer | |
| [[autodoc]] DDPOTrainer | |
| ## DPOTrainer | |
| [[autodoc]] DPOTrainer | |
| ## IterativeSFTTrainer | |
| [[autodoc]] IterativeSFTTrainer | |
| ## KTOConfig | |
| [[autodoc]] KTOConfig | |
| ## KTOTrainer | |
| [[autodoc]] KTOTrainer | |
| ## ORPOConfig | |
| [[autodoc]] ORPOConfig | |
| ## ORPOTrainer | |
| [[autodoc]] ORPOTrainer | |
| ## PPOConfig | |
| [[autodoc]] PPOConfig | |
| ## PPOTrainer | |
| [[autodoc]] PPOTrainer | |
| ## RewardConfig | |
| [[autodoc]] RewardConfig | |
| ## RewardTrainer | |
| [[autodoc]] RewardTrainer | |
| ## SFTTrainer | |
| [[autodoc]] SFTTrainer | |
| ## set_seed | |
| [[autodoc]] set_seed | |