Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning
Authors: Zhenni Bi, Kai Han, Chuanjian Liu, Yehui Tang, Yunhe Wang
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the Fo T framework, combined with these strategies, significantly enhances the reasoning capabilities of LLMs, enabling them to solve complex tasks with greater precision and efficiency. We evaluate the proposed Fo T method on the widely-used LLM reasoning benchmarks including Game of 24, GSM8K and MATH. |
| Researcher Affiliation | Industry | 1Huawei Noah s Ark Lab. Correspondence to: Yehui Tang <EMAIL>, Yunhe Wang <Yunhe EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Forest of Tree (Fo T) Require: Input x, LLM pĪø, n reasoning trees {Ti()}, i = 1, 2, , n; |
| Open Source Code | No | Code will be available at https://github.com/iamhankai/Forest-of Thought. |
| Open Datasets | Yes | We evaluate the proposed Fo T method on the widely-used LLM reasoning benchmarks including Game of 24, GSM8K and MATH. For the Game of 24 (Yao et al., 2024), our Fo T is built using To T as the reasoning tree. In addition to the To T-based Fo T, we developed an MCTSr-based Fo T to address mathematical problems, including those from the GSM8K (Cobbe et al., 2021a) and MATH (Hendrycks et al., 2021b) benchmarks. |
| Dataset Splits | Yes | We removed the duplicate and unsolvable problems, leaving 95 problems as the test set. |
| Hardware Specification | Yes | We also gratefully acknowledge the support provided by Mind Spore, CANN (Compute Architecture for Neural Networks), and the Ascend AI Processor used in this research. |
| Software Dependencies | No | We also gratefully acknowledge the support provided by Mind Spore, CANN (Compute Architecture for Neural Networks), and the Ascend AI Processor used in this research. |
| Experiment Setup | Yes | In our experiment, we set the sampling temperature to 0.95. |