Eric Liu
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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.