Apple Derives Theoretical Limits of KL-Regularized Language Model Alignment
AI-generated summary Apple research has characterized the information-theoretic limits of KL-regularized language model alignment by deriving the maximum reward improvement achievable under a given KL-divergence budget. The study examines reinforcement learning alignment and best-of-N alignment to establish bounds on how much a model's outputs can be steered toward human preferences before capability degradation occurs.