Previous chapters addressed the core components of RLHF: Supervised Fine-Tuning (SFT), Reward Model (RM) training, and Proximal Policy Optimization (PPO) fine-tuning. Now, we focus on connecting these stages into a coherent system.

This chapter details the practical aspects of building and running the complete RLHF pipeline. You will learn how to:

• Structure the workflow, managing the sequence of operations and data flow between the SFT, RM, and PPO stages.
• Handle the various models involved: loading the base model, initializing the SFT model, using the reward model for scoring, and managing the policy and value models during RL updates.
• Implement the process of generating responses with the current policy model and scoring these responses using the trained reward model to provide the signal for PPO.
• Consider code organization for building a maintainable end-to-end RLHF system.

We will examine how data moves through the system and how models are loaded, used, and potentially synchronized, concluding with a practical exercise to run a simplified version of the full loop.