KL regularization enables Õ(1/n) convergence for offline Nash equilibria in zero-sum Markov games under unilateral concentrability via the ROSE framework and SOS-MD algorithm.
A rewriting system for convex optimization problems
5 Pith papers cite this work. Polarity classification is still indexing.
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Establishes almost sure convergence rates arbitrarily close to o(n^{1-2η}) for power-law rates η in (1/2,1) and o(n^{-1}) for harmonic rates in contractive stochastic approximation with Markovian noise.
AstroAlertBench evaluates multimodal LLMs on astronomical classification accuracy, reasoning, and honesty using real ZTF alerts, revealing that high accuracy often diverges from self-assessed reasoning quality.
Proposes OPMD algorithm achieving accelerated O(1/n) rates for offline Nash equilibrium learning in alpha-potential games via reference-anchored data coverage.
KL regularization enables pessimism-free offline learning in general-sum games, recovering regularized Nash equilibria at accelerated rate O(1/n) via GANE and converging to coarse correlated equilibria at standard rate O(1/sqrt(n)+1/T) via GAMD.
citing papers explorer
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AstroAlertBench: Evaluating the Accuracy, Reasoning, and Honesty of Multimodal LLMs in Astronomical Classification
AstroAlertBench evaluates multimodal LLMs on astronomical classification accuracy, reasoning, and honesty using real ZTF alerts, revealing that high accuracy often diverges from self-assessed reasoning quality.