{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Daily Paper Cast","title":"AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time","html":"","width":"100%","height":180,"duration":1257,"description":"\n 🤗 Upvotes: 63 | cs.CL\n\n Authors:\n Junyu Zhang, Runpei Dong, Han Wang, Xuying Ning, Haoran Geng, Peihao Li, Xialin He, Yutong Bai, Jitendra Malik, Saurabh Gupta, Huan Zhang\n\n Title:\n AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time\n\n Arxiv:\n http://arxiv.org/abs/2505.24863v1\n\n Abstract:\n This paper presents AlphaOne ($\\alpha$1), a universal framework for modulating reasoning progress in large reasoning models (LRMs) at test time. $\\alpha$1 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, $\\alpha$1 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 $\\alpha$1's superior reasoning capability and efficiency. Project page: https://alphaone-project.github.io/\n ","thumbnail_url":"https://img.transistorcdn.com/8lOVNnuwhrA3rxrDMv7Osu4j_t1-jORooO6NfGcQhcw/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81Zjg1/YzRhODczMDU4MmE4/OGMwN2FiNDlmYzI2/MDliMi5qcGVn.webp","thumbnail_width":300,"thumbnail_height":300}