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FlexLoop is proposed, a novel post-training framework that converts pretrained fixed-depth looped policies into depth-elastic policies that supports reliable inference across recurrent depths and enables state-wise adaptive inference through recurrent-depth consistency.
Deciding which of two agents is stronger means playing games until skill outweighs luck, and every game costs money, model inference, or expert time. Since the number of games needed is unknown, fixed-budget evaluations either keep paying after the result is settled or stop before the agents can be told apart, while na...
The authors' bounds match existing delay-free regret guarantees for Lipschitz bandits and characterize the additional $\tilde{O}(\sqrt{D})$ impact introduced by feedback delays.
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