AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
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/
683d0b3de2a7d8d9778bd141
Junyu Zhang
claimed_verified
2025-06-02T07:40:59.716Z
6719bfd07c6e6c83a388aeae
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Junyu Zhang
user
jyzhang1208
683d0b3de2a7d8d9778bd142
Runpei Dong
claimed_verified
2025-06-02T07:41:03.079Z
6201fc5d91d53938a6432fbf
https://cdn-avatars.huggingface.co/v1/production/uploads/6201fc5d91d53938a6432fbf/VLs8ZYaZrop4KBpZn53fH.jpeg
Runpei Dong
user
RunpeiDong
683d0b3de2a7d8d9778bd143
Han Wang
683d0b3de2a7d8d9778bd144
Xuying Ning
683d0b3de2a7d8d9778bd145
Haoran Geng
683d0b3de2a7d8d9778bd146
Peihao Li
683d0b3de2a7d8d9778bd147
Xialin He
683d0b3de2a7d8d9778bd148
Yutong Bai
683d0b3de2a7d8d9778bd149
Jitendra Malik
683d0b3de2a7d8d9778bd14a
Saurabh Gupta
683d0b3de2a7d8d9778bd14b
Huan Zhang
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
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/
AlphaOne dynamically modulates reasoning in large models by introducing $\alpha$ moment and Bernoulli process for slow thinking, improving efficiency and capability across diverse domains.
AlphaOne $\alpha$ moment Bernoulli stochastic process large reasoning models reasoning transition tokens end-of-thinking token monotonic scaling methods fast reasoning efficient answer generation