AI RESEARCH
Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability
arXiv CS.AI
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ArXi:2605.09214v1 Announce Type: cross \emph{Kullback-Leibler} (KL) regularization is ubiquitous in reinforcement learning algorithms in the form of \emph{reverse} or \emph{forward} KL. Recent studies have nstrated $\epsilon^{-1}$-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.