AlphaOne:测试时慢速与快速推理的思维模型
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time
May 30, 2025
作者: Junyu Zhang, Runpei Dong, Han Wang, Xuying Ning, Haoran Geng, Peihao Li, Xialin He, Yutong Bai, Jitendra Malik, Saurabh Gupta, Huan Zhang
cs.AI
摘要
本文提出AlphaOne(alpha1),一种在测试时调节大型推理模型(LRMs)推理进程的通用框架。alpha1首先引入了alpha时刻,该时刻通过一个通用参数alpha来表征缩放后的思考阶段。在此预alpha时刻的缩放阶段内,它通过将推理过渡令牌的插入建模为伯努利随机过程,动态地调度慢速思维的转换。alpha时刻之后,alpha1确定性地以思考结束令牌终止慢速思维,从而促进快速推理和高效答案生成。此方法通过实现灵活且密集的慢速到快速推理调节,统一并推广了现有的单调缩放方法。在数学、编程及科学领域的一系列挑战性基准上的广泛实证研究,展示了alpha1卓越的推理能力与效率。项目页面:https://alphaone-project.github.io/
English
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/