@inproceedings{shi-etal-2025-speccot,
    title = "{S}pec{C}o{T}: Accelerating Chain-of-Thought Reasoning through Speculative Exploration",
    author = "Shi, Junhan  and
      Zhu, Yijia  and
      Shi, Zhenning  and
      Zhao, Dan  and
      Li, Qing  and
      Jiang, Yong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.1326/",
    doi = "10.18653/v1/2025.findings-emnlp.1326",
    pages = "24405--24415",
    ISBN = "979-8-89176-335-7",
    abstract = "Large Reasoning Models (LRMs) demonstrate strong performance on complex tasks through chain-of-thought (CoT) reasoning. However, they suffer from high inference latency due to lengthy reasoning chains. In this paper, we propose SpecCoT, a collaborative framework that combines large and small models for effective yet efficient reasoning. Unlike traditional speculative decoding, which operates at the token level, SpecCoT adopts a step-level verification strategy: the large model first establishes the reasoning direction, and for each intermediate step, the small model generates multiple candidate drafts in parallel. The large model then verifies these drafts, either selecting the most suitable one or rejecting them all and generating its own. SpecCoT approach balances reasoning quality with inference efficiency through fine-grained model cooperation. Experiments across diverse tasks show SpecCoT reduces inference latency by 1.7-4.1$\times$ while maintaining comparable accuracy to standard large model inference."
}