With recent advancements in large language models, methods like chain-of-thought prompting to elicit reasoning chains have been shown to improve results on reasoning tasks. However, tasks that require multiple steps of reasoning still pose significant challenges to state-of-the-art models. Drawing inspiration from the beam search algorithm, we propose PathFinder, a tree-search-based reasoning path generation approach. It enhances diverse branching and multi-hop reasoning through the integration of dynamic decoding, enabled by varying sampling methods and parameters. Using constrained reasoning, PathFinder integrates novel quality constraints, pruning, and exploration methods to enhance the efficiency and the quality of generation. Moreover, it includes scoring and ranking features to improve candidate selection. Our approach outperforms competitive baselines on three complex arithmetic and commonsense reasoning tasks by 6% on average. Our model generalizes well to longer, unseen reasoning chains, reflecting similar complexities to beam search with large branching factors.

翻译：随着大型语言模型的最新进展，链式思维提示等激发推理链的方法已被证明能提升推理任务的效果。然而，需要多步推理的任务对最先进的模型仍构成重大挑战。受束搜索算法的启发，我们提出了PathFinder——一种基于树搜索的推理路径生成方法。该方法通过整合动态解码（借助不同的采样方法和参数）来增强多样化分支与多跳推理能力。PathFinder利用约束推理，结合新颖的质量约束、剪枝和探索方法，提升生成效率与质量。此外，它包含评分与排序功能以改进候选选择。在三个复杂算术与常识推理任务上，我们的方法平均比竞争基线高出6%。该模型能够良好泛化到更长、未见过的推理链上，其特性与具有大分支因子的束搜索的复杂性相似。