This chapter examines Reinforcement Learning from Human Feedback (RLHF), a technique used to align Large Language Models more closely with human intentions. We will break down the standard RLHF pipeline, starting with how human preference data is gathered and prepared.

You will study the process of training a reward model, often represented as $r_\theta(x, y)$, designed to score outputs based on collected preferences. Following this, we will cover how this reward model guides the fine-tuning of the LLM's policy, denoted as $\pi_\phi(y|x)$, using reinforcement learning algorithms like Proximal Policy Optimization (PPO).

The sections will detail reward modeling architectures, loss functions, and common difficulties such as model calibration. We will also look into the specifics of PPO implementation for LLMs, including hyperparameter tuning and stability analysis. Finally, we address the limitations of RLHF and provide a practical exercise focused on implementing key parts of the process.