Reasoning Models Thinking Slow and Fast at Test Time

AlphaOne

University of Illinois Urbana-Champaign UC Berkeley

^†Equal contribution

EMNLP 2025 Main

alphaone-teaser

Overview

Key Takeaways

We present some insightful findings from evaluating three different LRMs, ranging from 1.5B to 32B across six reasoning benchmarks, including math, code generation, and scientific problem reasoning.

💡 Slow thinking first, then fast thinking, leads to better LRM reasoning.

💡 Slow thinking can bring efficient test-time scaling.

💡 Slow thinking transitioning in high frequency is helpful.

Experiments

Case Study

Success Examples

Failure Examples

BibTeX

        @article{AlphaOne25,
          title={AlphaOne: 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},
          journal={arXiv preprint arXiv:2505.24863},
          year={2025}
        }