This commit introduces a new blog post detailing the Proximal Policy Optimization (PPO) algorithm as used in Reinforcement Learning from Human Feedback (RLHF) for Large Language Models (LLMs).
The post covers:
- The mapping of RL concepts to text generation.
- The roles of the Actor, Critic, and Reward Model.
- The use of Generalized Advantage Estimation (GAE) for stable credit assignment.
- The PPO clipped surrogate objective for safe policy updates.
- The importance of pretraining loss to prevent catastrophic forgetting.
- The full iterative training loop.
