Apple paper derives theoretical limits of LM alignment
Read original source →machinelearning.apple.com
Apple researchers give a closed-form expression for maximum reward gain under a fixed KL budget, governed by a Jeffreys divergence term rather than the KL used in prior analyses. On safety and summarization tasks, best-of-N closely approaches the theoretical limit while PPO and GRPO remain substantially suboptimal.
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