Having established a method for modeling human preferences, the next step is to use this signal to directly refine the language model's behavior. This chapter concentrates on the Reinforcement Learning (RL) fine-tuning stage, specifically utilizing Proximal Policy Optimization (PPO). PPO is a policy gradient method commonly employed in RLHF to optimize the language model policy against the learned reward model while ensuring it doesn't stray too far from the original supervised fine-tuned model.

You will learn how to apply the PPO algorithm in the context of large language models. We will examine the setup of policy and value networks, the critical role of the KL divergence penalty ($D_{KL}$) for stable training, methods for calculating advantages like Generalized Advantage Estimation (GAE), and practical considerations for hyperparameter tuning. We will also look at implementation using libraries like Hugging Face's TRL and discuss common challenges like training instability. The objective is to equip you with the knowledge to implement and manage the PPO-based optimization phase of the RLHF pipeline.