Online mistake bounds for autoregressive output learning can grow logarithmically with generation horizon M under end-to-end feedback but become independent of M with chain-of-thought trajectory access.
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A Theory of Online Learning with Autoregressive Chain-of-Thought Reasoning
Online mistake bounds for autoregressive output learning can grow logarithmically with generation horizon M under end-to-end feedback but become independent of M with chain-of-thought trajectory access.