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Behavior policies trained in simulation can be robust to real-world transition uncertainties, reducing evaluation variance and reliance on costly real-world samples.
LLMs can be trained to negotiate like expert agents, extracting significantly higher surpluses by strategically exploring buyer markets rather than fixating on immediate bids.
Ditching pessimism unlocks a quadratically faster, $\widetilde{\mathcal{O}}(1/n)$ sample complexity for offline learning in KL-regularized games.
