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Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning invalid reasoning paths early, leading to significantly improved reasoning efficiency and more favorable test-time cost scaling. Despite strong empirical success, the theo...
This work studies how many samples are necessary and sufficient to learn an $\varepsilon$-optimal robust policy under the average-reward criterion and achieves these rates using reduction-based plug-in procedures that select the reduction---nominal or robust---and its discount factor.
This work presents a new lower bound of $\Omega(T^{-1.9319})$ for the last-iterate convergence rate of gradient descent with predetermined nonnegative stepsize schedules, and provides rigorous evidence that stepsize schedules alone cannot accelerate plain GD to the optimal $O(T^{-2})$ convergence rate.
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