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Achieving a total-sample rate improvement from $T^{-1/4+o(1)}$ to $T^{-1/2+o(1)}$ could redefine efficiency benchmarks in stochastic approximation methods.
Achieving near-optimal regret in continuous dueling bandits is now possible with just logarithmic space complexity, opening the door to efficient exploration in complex comparative decision-making problems.
