# 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