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AI CURATED HOT STORY · 11/12/2026

Study Derives Information-Theoretic Limits of KL-Regularized Alignment

1 reports archived1 independent sourcesupdated 11/12/2026, 08:00:00
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The paper characterizes the information-theoretic limits of KL-regularized alignment, deriving a closed-form expression for the maximum achievable expected reward gain, which is governed by a Jeffreys divergence term rather than the √KL term used in prior analyses. The authors reformulate this as a covariance under the base model, yielding a practical estimator that predicts alignment gains from base-model samples alone, and extend the analysis to proxy rewards, showing reward hacking grows with reward error.

LATEST/The paper characterizes the information-theoretic limits of KL-regularized alignment, deriving a closed-form expression for the maximum achievable expected reward gain, which is governed by a Jeffreys divergence term rather than the √KL term used in prior analyses. The authors reformulate this as a covariance under the base model, yielding a practical estimator that predicts alignment gains from base-model samples alone, and extend the analysis to proxy rewards, showing reward hacking grows with reward error.

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Total 1 reports · Latest first
  1. Apple Machine Learning ResearchT1·71 pts

    Study Derives Information-Theoretic Limits of KL-Regularized Alignment

    • Optimal reward gain under a KL budget is governed by Jeffreys divergence, not the √KL term used previously
    • The bound can be rewritten as a covariance under the base model, enabling gain prediction from base-model samples alone
    • In proxy-reward settings, the ideal-versus-proxy gap (reward hacking) grows with reward error magnitude