@inproceedings{jiang-etal-2025-logicpro,
    title = "{L}ogic{P}ro: Improving Complex Logical Reasoning via Program-Guided Learning",
    author = "Jiang, Jin  and
      Yan, Yuchen  and
      Liu, Yang  and
      Wang, Jianing  and
      Peng, Shuai  and
      Cai, Xunliang  and
      Cao, Yixin  and
      Zhang, Mengdi  and
      Gao, Liangcai",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1270/",
    pages = "26200--26218",
    ISBN = "979-8-89176-251-0",
    abstract = "In this paper, we propose a new data synthesis method called \textbf{LogicPro}, which leverages LeetCode-style algorithm Problems and their corresponding Program solutions to synthesize Complex Logical Reasoning data in text format. First, we synthesize complex reasoning problems through source algorithm problems and test cases. Then, standard answers and intermediate variable outputs are obtained for each problem based on standard python solutions and test cases. Finally, with the guidance of code intermediate variables, we synthesize the text reasoning process for each reasoning problems. Through this method, we can synthesize data that is difficult, scalable, effective, and comes with golden standard answers and high-quality reasoning processes. As a result, with our 540K synthesized dataset constructed solely from 2,360 algorithm problems, our approach achieves significant improvements in multiple models for the datasets \textit{BBH{\textasciicircum}27}, \textit{LogicBench}, \textit{DROP}, \textit{AR-LSAT}, and \textit{GSM8K}, etc. outperforming a wide range of existing reasoning datasets."
}