@inproceedings{he-etal-2025-breaking,
    title = "Breaking the Reasoning Barrier A Survey on {LLM} Complex Reasoning through the Lens of Self-Evolution",
    author = "He, Tao  and
      Li, Hao  and
      Chen, Jingchang  and
      Liu, Runxuan  and
      Cao, Yixin  and
      Liao, Lizi  and
      Zheng, Zihao  and
      Chu, Zheng  and
      Liang, Jiafeng  and
      Liu, Ming  and
      Qin, Bing",
    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.386/",
    pages = "7377--7417",
    ISBN = "979-8-89176-256-5",
    abstract = "The release of OpenAI{'}s O1 and subsequent projects like DeepSeek R1 has significantly advanced research on complex reasoning in LLMs. This paper systematically analyzes existing reasoning studies from the perspective of self-evolution, structured into three components: data evolution, model evolution, and self-evolution. Data evolution explores methods to generate higher-quality reasoning training data. Model evolution focuses on training strategies to boost reasoning capabilities. Self-evolution research autonomous system evolution via iterating cycles of data and model evolution. We further discuss the scaling law of self-evolution and analyze representative O1-like works through this lens. By summarizing advanced methods and outlining future directions, this paper aims to drive advancements in LLMs' reasoning abilities."
}