@inproceedings{sun-etal-2025-self,
    title = "The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding?",
    author = "Sun, Yutao  and
      Chen, Mingshuai  and
      Zhao, Tiancheng  and
      Xu, Ruochen  and
      Zhang, Zilun  and
      Yin, Jianwei",
    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.337/",
    pages = "6501--6512",
    ISBN = "979-8-89176-256-5",
    abstract = "Self-improving large language models (LLMs) {--} i.e., to improve the performance of an LLM by fine-tuning it with synthetic data generated by itself {--} is a promising way to advance the capabilities of LLMs while avoiding extensive supervision. Existing approaches to self-improvement often rely on external supervision signals in the form of seed data and/or assistance from third-party models. This paper presents Crescent {--} a simple yet effective framework for generating high-quality synthetic question-answer data in a fully autonomous manner. Crescent first elicits the LLM to generate raw questions via a bait prompt, then diversifies these questions leveraging a rejection sampling-based self-deduplication, and finally feeds the questions to the LLM and collects the corresponding answers by means of majority voting. We show that Crescent sheds light on the potential of true self-improvement with zero external supervision signals for math reasoning; in particular, Crescent-generated question-answer pairs suffice to (i) improve the reasoning capabilities of an LLM while preserving its general performance (especially in the 0-shot setting); and (ii) distill LLM knowledge to weaker models more effectively than existing methods based on seed-dataset augmentation."
}