@inproceedings{zhang-etal-2025-alphaone,
    title = "{A}lpha{O}ne: Reasoning Models Thinking Slow and Fast at Test Time",
    author = "Zhang, Junyu  and
      Dong, Runpei  and
      Wang, Han  and
      Ning, Xuying  and
      Geng, Haoran  and
      Li, Peihao  and
      He, Xialin  and
      Bai, Yutong  and
      Malik, Jitendra  and
      Gupta, Saurabh  and
      Zhang, Huan",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.570/",
    pages = "11340--11365",
    ISBN = "979-8-89176-332-6",
    abstract = "This paper presents AlphaOne ($\alpha1$), a universal framework for modulating reasoning progress in large reasoning models (LRMs) at test time. $\alpha1$ first introduces $\alpha$ moment, which represents the scaled thinking phase with a universal parameter $\alpha$.Within this scaled pre-$\alpha$ moment phase, it dynamically schedules slow thinking transitions by modeling the insertion of reasoning transition tokens as a Bernoulli stochastic process. After the $\alpha$ moment, $\alpha1$ deterministically terminates slow thinking with the end-of-thinking token, thereby fostering fast reasoning and efficient answer generation. This approach unifies and generalizes existing monotonic scaling methods by enabling flexible and dense slow-to-fast reasoning modulation. Extensive empirical studies on various challenging benchmarks across mathematical, coding, and scientific domains demonstrate $\alpha1${`}s superior reasoning capability and efficiency. Project page: https://alphaone-project.github.io/."
}