AINeutralarXiv – CS AI · 7h ago6/10
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Annealed Softmax Greedy in Many-Armed Bayesian Bandits
This paper analyzes why reinforcement learning methods that update policies based on reward signals without explicitly tracking uncertainty can still be effective. Researchers prove that annealed softmax policies achieve near-optimal regret rates in many-armed Bayesian bandit settings when many near-optimal actions exist, providing theoretical justification for uncertainty-agnostic approaches used in modern language model training.