@inproceedings{bu-etal-2025-enhanced,
    title = "Enhanced Data Synthesis for {LLM} through Reasoning Structures Generated by Hierarchical {GF}low{N}et",
    author = "Bu, Tianpeng  and
      Zhang, Minying  and
      Duan, Hongtao  and
      Li, Shurui  and
      Hu, Lulu  and
      Li, Yu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.821/",
    pages = "15931--15958",
    ISBN = "979-8-89176-256-5",
    abstract = "Large language models (LLMs) excel in problem-solving but require training data with diverse reasoning processes. Existing methods mainly optimize instruction-response pairs but lack a systematic design for the underlying reasoning structure. This paper proposes RSS: a Reasoning Structure driven data Synthesis method. We first proactively develop a hierarchical GFlowNet to construct reasoning structures efficiently through a coarse-to-fine directed acyclic graph (DAG) growth process. Then reasoning DAGs are leveraged to actively guide the instruction generation via an iterative suggester-editor workflow and enhance response quality using a structure-aware strategy. Experiments show that LLMs trained on our synthetic datasets achieve 48.50{\%}, 84.00{\%}, 79.90{\%} for AlpacaEval2, GSM8K and HumanEval, outperforming existing data synthesis methods."
}