LiteSearch：用于LLM的高效树搜索

LiteSearch: Efficacious Tree Search for LLM

June 29, 2024

作者: Ante Wang, Linfeng Song, Ye Tian, Baolin Peng, Dian Yu, Haitao Mi, Jinsong Su, Dong Yu

cs.AI

摘要

最近的研究表明，树搜索算法（例如蒙特卡洛树搜索）可以显著提升LLM在复杂数学推理任务上的性能。然而，由于浪费性的搜索策略，它们通常需要超过贪婪解码超过10倍的计算资源，这使得它们难以在实际应用中部署。本研究引入了一种新颖的引导树搜索算法，具有动态节点选择和节点级别探索预算（最大子节点数量）计算，以解决这一问题。通过考虑朝着最终答案（历史）的搜索进展和来自值网络（未来）的指导，该算法在分配的计算预算范围内迭代选择最有前途的树节点，然后扩展它。在GSM8K和TabMWP数据集上进行的实验表明，我们的方法不仅提供了竞争性的性能，而且与基线方法相比，计算成本显著降低。

English

Recent research suggests that tree search algorithms (e.g. Monte Carlo Tree Search) can dramatically boost LLM performance on complex mathematical reasoning tasks. However, they often require more than 10 times the computational resources of greedy decoding due to wasteful search strategies, making them difficult to be deployed in practical applications. This study introduces a novel guided tree search algorithm with dynamic node selection and node-level exploration budget (maximum number of children) calculation to tackle this issue. By considering the search progress towards the final answer (history) and the guidance from a value network (future) trained without any step-wise annotations, our algorithm iteratively selects the most promising tree node before expanding it within the boundaries of the allocated computational budget. Experiments conducted on the GSM8K and TabMWP datasets demonstrate that our approach not only offers competitive performance but also enjoys significantly lower computational costs compared to baseline methods.