Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback

Understand

Reward models trained through Reinforcement Learning from AI Feedback (RLAIF) methods frequently suffer from limited generalizability, which hinders the alignment performance of policy models.

• This challenge stems from various issues, including distribution shift, preference label noise, and mismatch of overly challenging samples with model capacity.
• In this paper, we aim to enhance the generalizability of reward models through a data-centric approach, driven by the insight that these issues are inherently intertwined from a uniform perspective of data difficulty.
• Accordingly, we propose a novel framework, Curriculum-RLAIF, which constructs preference pairs with varying difficulty levels and then produces a specific curriculum for reward model training.