Having established the standard three-stage Reinforcement Learning from Human Feedback (RLHF) pipeline using Proximal Policy Optimization (PPO), we now turn our attention to more advanced methods and alternatives. While the PPO-based approach is effective, ongoing research has produced techniques that can offer advantages in terms of stability, sample efficiency, or by simplifying the overall process.

This chapter examines several of these cutting-edge techniques. We will analyze Direct Preference Optimization (DPO), a method that bypasses the need for an explicit reward model by directly optimizing the language model policy based on preference data. We'll also discuss Reinforcement Learning from AI Feedback (RLAIF), where AI models substitute for human annotators in generating preference labels. Furthermore, we will cover strategies specifically aimed at improving the efficiency of RLHF training, methods for detecting and mitigating reward hacking, the concept of multi-objective reward modeling, and how RLHF can be adapted for contextual scenarios. Understanding these advanced approaches provides a more comprehensive view of the techniques available for aligning language models.