LiteSearch: Efficacious Tree Search for LLM
Ante Wang, Linfeng Song, Ye Tian, Baolin Peng, Dian Yu, Haitao Mi,, Jinsong Su, Dong Yu

TL;DR
LiteSearch introduces a guided tree search algorithm for LLMs that improves reasoning performance while significantly reducing computational costs, making tree search more practical for real-world applications.
Contribution
It presents a novel guided tree search method with dynamic node selection and exploration budgeting, trained without step-wise annotations, enhancing efficiency and effectiveness.
Findings
Achieves competitive accuracy on GSM8K and TabMWP datasets.
Reduces computational costs compared to baseline tree search methods.
Demonstrates practical applicability of tree search in LLM reasoning tasks.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNatural Language Processing Techniques · Semantic Web and Ontologies · Data Mining Algorithms and Applications
