LLM warm-starts for bandits remain better than cold-starts up to roughly 30% random label noise but increase regret under systematic misalignment, with a derived sufficient condition on prior error that predicts when the warm-start helps.
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L
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Jump Start or False Start? A Theoretical and Empirical Evaluation of LLM-initialized Bandits
LLM warm-starts for bandits remain better than cold-starts up to roughly 30% random label noise but increase regret under systematic misalignment, with a derived sufficient condition on prior error that predicts when the warm-start helps.