Near-Optimal Regret for KL-Regularized Multi-Armed Bandits.

arXiv:2603.02155v2 Announce Type: replace-cross Abstract: Recent studies have shown that reinforcement learning with KL-regularized objectives can enjoy faster rates of convergence or logarithmic regret, in contrast to the classical $\sqrt{T}$-type regret in t