Theoretical Limits of Language Model Alignment

Researchers at Apple have published an analysis detailing the fundamental theoretical constraints inherent in common language model alignment methods like reinforcement learning and best-of-N sampling. The study demonstrates that these alignment techniques inevitably lead to a trade-off between refining model outputs to match human preferences and maintaining the original performance capabilities of the base model. This finding provides a mathematical framework for developers to better understand the limitations of current tuning strategies when balancing safety, utility, and factual accuracy.