@inproceedings{chen-etal-2025-flaming,
    title = "Flaming-hot Initiation with Regular Execution Sampling for Large Language Models",
    author = "Chen, Weizhe  and
      Zhang, Zhicheng  and
      Liu, Guanlin  and
      Zheng, Renjie  and
      Shi, Wenlei  and
      Dun, Chen  and
      Wu, Zheng  and
      Jin, Xing  and
      Yan, Lin",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.396/",
    pages = "7118--7127",
    ISBN = "979-8-89176-195-7",
    abstract = "Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains. A key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data. This becomes especially critical in reasoning-related tasks with sandbox checkers, such as math or code, where the goal is to generate correct solutions to specific problems with higher probability. In this work, we introduce Flaming-hot Initiation with Regular Execution (FIRE) sampling, a simple yet highly effective method to efficiently find good responses. Our empirical findings show that FIRE sampling enhances inference-time generation quality and also benefits training in the alignment stage. Furthermore, we explore how FIRE sampling improves performance by promoting diversity and analyze the impact of employing FIRE at different positions within a response."
}