LiteSearch: Efficacious Tree Search for LLM
- URL: http://arxiv.org/abs/2407.00320v1
- Date: Sat, 29 Jun 2024 05:14:04 GMT
- Title: LiteSearch: Efficacious Tree Search for LLM
- Authors: Ante Wang, Linfeng Song, Ye Tian, Baolin Peng, Dian Yu, Haitao Mi, Jinsong Su, Dong Yu,
- Abstract summary: This study introduces a novel guided tree search algorithm with dynamic node selection and node-level exploration budget.
Experiments conducted on the GSM8K and TabMWP datasets demonstrate that our approach enjoys significantly lower computational costs compared to baseline methods.
- Score: 70.29796112457662
- License: http://creativecommons.org/licenses/by/4.0/
- 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 demonstrate that our approach not only offers competitive performance but also enjoys significantly lower computational costs compared to baseline methods.
Related papers
- Don't Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls [83.89771461061903]
Recent advancements in tree search algorithms guided by verifiers have significantly enhanced the reasoning capabilities of large language models (LLMs)
Recent advancements in tree search algorithms guided by verifiers have significantly enhanced the reasoning capabilities of large language models (LLMs)
We identify two key challenges contributing to this inefficiency: $textitover-exploration$ due to redundant states with semantically equivalent content, and $textitunder-exploration$ caused by high variance in verifier scoring.
We propose FETCH, a flexible, plug-and-play system compatible with various tree search algorithms.
arXiv Detail & Related papers (2025-02-16T16:12:01Z) - Enhancing LLM Reasoning with Reward-guided Tree Search [95.06503095273395]
o1-like reasoning approach is challenging, and researchers have been making various attempts to advance this open area of research.
We present a preliminary exploration into enhancing the reasoning abilities of LLMs through reward-guided tree search algorithms.
arXiv Detail & Related papers (2024-11-18T16:15:17Z) - Autonomous Tree-search Ability of Large Language Models [58.68735916408101]
Large Language Models have excelled in remarkable reasoning capabilities with advanced prompting techniques.
Recent works propose to utilize external programs to define search logic, such that LLMs can perform passive tree search to solve more challenging reasoning tasks.
We propose a new concept called autonomous tree-search ability of LLM, which can automatically generate a response containing search trajectories for the correct answer.
arXiv Detail & Related papers (2023-10-14T14:14:38Z) - Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models [17.059322033670124]
We propose a novel strategy that propels Large Language Models through algorithmic reasoning pathways.
Our results suggest that instructing an LLM using an algorithm can lead to performance surpassing that of the algorithm itself.
arXiv Detail & Related papers (2023-08-20T22:36:23Z) - UNSAT Solver Synthesis via Monte Carlo Forest Search [10.754275929551593]
We introduce Monte Carlo Forest Search (MCFS), a class of reinforcement learning (RL) algorithms for learning policies in tree MDPs
Examples of such problems include proving unsatisfiability of a SAT formula; counting the number of solutions of a satisfiable SAT formula.
We dub Knuth Synthesis, an MCFS algorithm that learns DPLL branching policies for solving the satisfiability (SAT) problem.
arXiv Detail & Related papers (2022-11-22T20:52:50Z) - Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
This paper investigates the classical integer least-squares problem which estimates signals integer from linear models.
The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning.
We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
arXiv Detail & Related papers (2021-01-07T08:00:02Z) - MurTree: Optimal Classification Trees via Dynamic Programming and Search [61.817059565926336]
We present a novel algorithm for learning optimal classification trees based on dynamic programming and search.
Our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances.
arXiv Detail & Related papers (2020-07-24T17:06:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.