@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://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.683/",
    pages = "13523--13564",
    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."
}