AI RESEARCH
Demystifying the unreasonable effectiveness of online alignment methods
arXiv CS.LG
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ArXi:2604.17207v1 Announce Type: new 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