Training

Proximal Policy Optimization (PPO)

PPO is an RL algorithm that optimizes policies (models) based on reward signals. It's used in RLHF training to optimize the language model based on reward model predictions. PPO is relatively stable and sample-efficient compared to other RL algorithms. It uses a clipping mechanism to prevent large policy updates. PPO is the standard RL algorithm in LLM training. Understanding PPO is helpful for understanding RLHF. PPO requires careful hyperparameter tuning.