Demystifying the unreasonable effectiveness of online alignment methods
Iterative alignment methods based on purely greedy updates are remarkably effective in practice, yet existing theoretical guarantees of \(O(\log T)\) KL-regularized regret can seem pessimistic relative to their empirical performance. In this paper, we argue that this mismatch arises from the regret criterion itself: KL-regularized regret conflates the statistical cost of learning with the exploratory randomization induced by the softened training policy. To separate these effects, we study the t
Record details
Published: 19 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (19 April 2026), “Demystifying the unreasonable effectiveness of online alignment methods,” evidence record 5677, https://ethics.ai/record/5677 (originally published by arXiv).
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