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A practical guide to reasoning models — LLMs that do extra work before they answer. What people actually mean by a "reasoning model," how reinforcement learning on checkable problems trains the behavior in, what inference-time compute is and why spending more of it raises accuracy, whether a visible chain of thought can be trusted, which tasks the extra thinking helps and which it wastes, and how to control how much a model thinks.
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