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arXiv · 2404.05966

THOUGHTSCULPT: Reasoning with Intermediate Revision and Search

Yizhou Chi·Kevin Yang·Dan Klein
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Abstract

We present THOUGHTSCULPT, a general reasoning and search method for tasks with outputs that can be decomposed into components. THOUGHTSCULPT explores a search tree of potential solutions using Monte Carlo Tree Search (MCTS), building solutions one action at a time and evaluating according to any domain-specific heuristic, which in practice is often simply an LLM evaluator. Critically, our action space includes revision actions: THOUGHTSCULPT may choose to revise part of its previous output rather than continuing to build the rest of its output. Empirically, THOUGHTSCULPT outperforms state-of-the-art reasoning methods across three challenging tasks: Story Outline Improvement (up to +30% interestingness), Mini-Crosswords Solving (up to +16% word success rate), and Constrained Generation (up to +10% concept coverage).

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Yizhou Chi, Kevin Yang, Dan Klein. 2024-04-09. THOUGHTSCULPT: Reasoning with Intermediate Revision and Search. https://arxiv.org/abs/2404.05966

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