arXiv: cs.AI preprintsInternational6 November 2026
Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability
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arXiv:2605.09214v2 Announce Type: replace-cross Abstract: \emph{Kullback-Leibler} (KL) regularization is ubiquitous in reinforcement learning algorithms in the form of \emph{reverse} or \emph{forward} KL. Recent studies have demonstrated $\epsilon^{-1}$-type fast rates for decision making under reverse KL regularization, in contrast to the standard $\epsilon^{-2}$-type sample complexity. However, for forward-KL-regularized objectives, existing statistical analyses are either not applicable or result in $\tilde{O}(\epsilon^{-2})$ slow rates. We take the first step towards addressing this proble
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