title: AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
summary: 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/
paper_authors._id: 683d0b3de2a7d8d9778bd141
paper_authors.name: Junyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:40:59.716Z
paper_authors._id: 6719bfd07c6e6c83a388aeae
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6719bfd07c6e6c83a388aeae/jHxryk04dzHo23TX5F5sz.png
paper_authors.fullname: Junyu Zhang
paper_authors.type: user
paper_authors.user: jyzhang1208
paper_authors._id: 683d0b3de2a7d8d9778bd142
paper_authors.name: Runpei Dong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:41:03.079Z
paper_authors._id: 6201fc5d91d53938a6432fbf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6201fc5d91d53938a6432fbf/VLs8ZYaZrop4KBpZn53fH.jpeg
paper_authors.fullname: Runpei Dong
paper_authors.type: user
paper_authors.user: RunpeiDong
paper_authors._id: 683d0b3de2a7d8d9778bd143
paper_authors.name: Han Wang
paper_authors._id: 683d0b3de2a7d8d9778bd144
paper_authors.name: Xuying Ning
paper_authors._id: 683d0b3de2a7d8d9778bd145
paper_authors.name: Haoran Geng
paper_authors._id: 683d0b3de2a7d8d9778bd146
paper_authors.name: Peihao Li
paper_authors._id: 683d0b3de2a7d8d9778bd147
paper_authors.name: Xialin He
paper_authors._id: 683d0b3de2a7d8d9778bd148
paper_authors.name: Yutong Bai
paper_authors._id: 683d0b3de2a7d8d9778bd149
paper_authors.name: Jitendra Malik
paper_authors._id: 683d0b3de2a7d8d9778bd14a
paper_authors.name: Saurabh Gupta
paper_authors._id: 683d0b3de2a7d8d9778bd14b
paper_authors.name: Huan Zhang
paper_title: AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
paper_summary: 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/
paper_ai_summary: AlphaOne dynamically modulates reasoning in large models by introducing $\alpha$ moment and Bernoulli process for slow thinking, improving efficiency and capability across diverse domains.
paper_ai_keywords: AlphaOne $\alpha$ moment Bernoulli stochastic process large reasoning models reasoning transition tokens end-of-thinking token monotonic scaling methods fast reasoning efficient answer generation