This chapter lays the groundwork for understanding Reinforcement Learning from Human Feedback (RLHF) as applied to large language models (LLMs). We begin by examining the core challenge: aligning LLMs with human intent and values, a task that standard supervised fine-tuning often falls short of achieving completely.

You will learn about:

• The specific nature of the AI alignment problem as it pertains to LLMs.
• Why conventional Supervised Fine-Tuning (SFT) is insufficient for nuanced alignment tasks.
• A review of essential Reinforcement Learning (RL) concepts, particularly Proximal Policy Optimization (PPO), which are fundamental to RLHF.
• An initial overview of the standard three-stage RLHF process: SFT, Reward Modeling, and RL Fine-Tuning.
• Guidance on setting up the required software environment and libraries for the practical exercises throughout the course.

By the end of this chapter, you will have a clear understanding of why RLHF is necessary and the basic components involved, preparing you for the detailed implementation discussions in subsequent chapters.