@Kevin_GuoweiXu: How should LLMs sample on hard reasoning problems during post-training and inference where direct rollouts rarely produ…
Summary
Introduces BES (Bidirectional Evolutionary Search), a search framework for LLMs that combines forward candidate evolution with backward goal decomposition to improve sampling on hard reasoning problems during post-training and inference.
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@lateinteraction: very cool work !!
Guowei Xu discusses limitations of Best-of-N and tree search methods for LLMs on hard reasoning problems, noting sparse verification signals and that candidates remain within the model's distribution.
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