Apple paper derives information-theoretic limits of reward gains under KL-regularized alignment
Apple ML Research · rss · 2026-11-12
Apple ML Research published "Theoretical Limits of Language Model Alignment," characterizing the information-theoretic limits of KL-regularized alignment. The paper covers the two dominant alignment approaches—RL maximizing expected reward under a KL-divergence constraint, and best-of-N selection—and derives the maximum achievable reward improvement given a KL budget, filling a gap in the fundamental understanding of alignment methods.
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