HyperGuide: Hyperbolic Guidance for Efficient Multi-Step Reasoning in Large Language Models

arXiv:2605.24140v5 Announce Type: replace Abstract: Searching over alternative continuations lets large language models (LLMs) compare the downstream consequences of a reasoning step before committing to it, but expanding and evaluating many branches makes inference expensive. Training value models to score intermediate steps does not avoid this cost, as they still rank candidates at inference time. We introduce HyperGuide, a framework that treats reasoning as movement through a tree of states embedded in hyperbolic space. HyperGuide first trains a state encoder on the Poincar'e ball, then tra

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