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News/Continuity-Free Near-Minimax Leading-Order Regret for CVaR-UCBVI
arxiv
PublishedSeptember 1, 2026 at 4:00 AM

Continuity-Free Near-Minimax Leading-Order Regret for CVaR-UCBVI

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Publisher summary· verbatim

arXiv:2608.28960v1 Announce Type: new Abstract: For finite-horizon tabular CVaR reinforcement learning, prior work proves a $\widetilde{O}(\tau^{-1}\sqrt{SAK})$ leading regret bound for arbitrary normalized return laws and the sharper $\widetilde{O}(\sqrt{SAK/\tau})$ rate under a density lower bound

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