Building on the conceptual understanding of Reinforcement Learning from AI Feedback (RLAIF), this section details the practical steps for implementation. We will move from theory to constructing the components of an RLAIF pipeline.

You will learn the mechanics of building the AI preference labeler, managing the collected preference data, and training the preference model, often represented by a function $P(y_1 \succ y_2 | x)$, where $y_1$ and $y_2$ are potential responses to a prompt $x$. Subsequently, we will cover the setup and execution of the Proximal Policy Optimization (PPO) loop using the learned reward signal derived from this preference model. We will also address practical considerations such as hyperparameter tuning, methods for scaling the training process for large models and datasets, and identifying and rectifying typical problems encountered during RLAIF implementation.