@inproceedings{li-etal-2025-start,
title = "{START}: Self-taught Reasoner with Tools",
author = "Li, Chengpeng and
Xue, Mingfeng and
Zhang, Zhenru and
Yang, Jiaxi and
Zhang, Beichen and
Yu, Bowen and
Hui, Binyuan and
Lin, Junyang and
Wang, Xiang and
Liu, Dayiheng",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.683/",
doi = "10.18653/v1/2025.emnlp-main.683",
pages = "13512--13553",
ISBN = "979-8-89176-332-6",
abstract = "Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations. Integrating computational tools with LRMs remains challenging, particularly in activating and enhancing models' tool-use capabilities without compromising their reasoning strengths. We address these challenges through START (Self-taught Reasoner with Tools), introducing two key innovations: (1) Hint-infer, a training-free approach that activates LRMs' latent tool-use capabilities through artificial hints, enabling test-time performance scaling; (2) Hint-RFT, a self-training framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis. Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0{\%}, AIME24: 75.6{\%}) and graduate-level science questions (GPQA: 64.6{\%}). Our analysis reveals that START not only enhances accuracy but also improves reasoning efficiency through strategic tool utilization, demonstrating broad applicability in complex reasoning scenarios."
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<abstract>Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations. Integrating computational tools with LRMs remains challenging, particularly in activating and enhancing models’ tool-use capabilities without compromising their reasoning strengths. We address these challenges through START (Self-taught Reasoner with Tools), introducing two key innovations: (1) Hint-infer, a training-free approach that activates LRMs’ latent tool-use capabilities through artificial hints, enabling test-time performance scaling; (2) Hint-RFT, a self-training framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis. Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0%, AIME24: 75.6%) and graduate-level science questions (GPQA: 64.6%). Our analysis reveals that START not only enhances accuracy but also improves reasoning efficiency through strategic tool utilization, demonstrating broad applicability in complex reasoning scenarios.</abstract>
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%0 Conference Proceedings
%T START: Self-taught Reasoner with Tools
%A Li, Chengpeng
%A Xue, Mingfeng
%A Zhang, Zhenru
%A Yang, Jiaxi
%A Zhang, Beichen
%A Yu, Bowen
%A Hui, Binyuan
%A Lin, Junyang
%A Wang, Xiang
%A Liu, Dayiheng
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F li-etal-2025-start
%X Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations. Integrating computational tools with LRMs remains challenging, particularly in activating and enhancing models’ tool-use capabilities without compromising their reasoning strengths. We address these challenges through START (Self-taught Reasoner with Tools), introducing two key innovations: (1) Hint-infer, a training-free approach that activates LRMs’ latent tool-use capabilities through artificial hints, enabling test-time performance scaling; (2) Hint-RFT, a self-training framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis. Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0%, AIME24: 75.6%) and graduate-level science questions (GPQA: 64.6%). Our analysis reveals that START not only enhances accuracy but also improves reasoning efficiency through strategic tool utilization, demonstrating broad applicability in complex reasoning scenarios.
%R 10.18653/v1/2025.emnlp-main.683
%U https://aclanthology.org/2025.emnlp-main.683/
%U https://doi.org/10.18653/v1/2025.emnlp-main.683
%P 13512-13553
Markdown (Informal)
[START: Self-taught Reasoner with Tools](https://aclanthology.org/2025.emnlp-main.683/) (Li et al., EMNLP 2025)
ACL
- Chengpeng Li, Mingfeng Xue, Zhenru Zhang, Jiaxi Yang, Beichen Zhang, Bowen Yu, Binyuan Hui, Junyang Lin, Xiang Wang, and Dayiheng Liu. 2025. START: Self-taught Reasoner with Tools. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 13512–13553, Suzhou, China. Association for Computational Linguistics.