AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
Authors: name: Junyu Zhang, name: Runpei Dong, name: Han Wang, name: Xuying Ning, name: Haoran Geng, name: Peihao Li, name: Xialin He, name: Yutong Bai, name: Jitendra Malik, name: Saurabh Gupta, name: Huan Zhang

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/