Building on the need for alignment techniques that can operate at scale, this chapter introduces Reinforcement Learning from AI Feedback (RLAIF). Where Reinforcement Learning from Human Feedback (RLHF) relies on human annotators to create preference data, RLAIF substitutes this with AI-generated feedback. This approach aims to provide supervisory signals more efficiently than direct human labeling alone permits.

This chapter examines the mechanics of RLAIF. You will learn:

• The core concept of RLAIF and how it differs from the standard RLHF process.
• Strategies for generating AI preference labels, potentially using a guiding constitution or another aligned model.
• How to train a preference model $p_\theta(y_w \succ y_l | x)$ that learns to predict which of two responses ($y_w$, $y_l$) is better for a given prompt $x$, based on the AI-generated labels.
• Methods for converting the outputs of the preference model into a scalar reward signal $r(x, y)$ suitable for reinforcement learning.
• Considerations for applying RL algorithms, such as Proximal Policy Optimization (PPO), using the AI-generated reward signal.
• Common issues related to training stability and convergence when using AI feedback and techniques to mitigate them.
• The theoretical underpinnings and known limitations of the RLAIF methodology.