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City University of Hong Kong
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Overestimating test-time scaling rewards is mitigated by a novel Bayesian approach that explicitly models and corrects for both PRM and policy uncertainties.
Offline risk-sensitive RL is now provably efficient, with new algorithms and tight bounds for dynamic and static optimized certainty equivalents.