THOUGHTSCULPT: Reasoning with Intermediate Revision and Search
<|reference_start|>THOUGHTSCULPT: Reasoning with Intermediate Revision and Search: 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).<|reference_end|>
@article{chi2024thoughtsculpt:,
    title={THOUGHTSCULPT: Reasoning with Intermediate Revision and Search},
    author={Yizhou Chi, Kevin Yang, Dan Klein},
    journal={arXiv preprint arXiv:2404.05966},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05966},
    primaryClass={cs.CL cs.AI}
}