Core Viewpoint - The article discusses a new reasoning framework called AlphaOne, which suggests that AI models should adopt a "slow thinking first, fast thinking later" approach during testing, contrasting with the traditional human-like reasoning paradigm [4][5][6]. Group 1: Introduction of AlphaOne - AlphaOne introduces a global reasoning control hyperparameter α that allows models to switch from slow to fast reasoning without additional training, significantly improving reasoning accuracy and efficiency [6][12]. - The framework challenges the assumption that AI must think like humans, proposing a more effective reasoning strategy [6][4]. Group 2: Mechanism of AlphaOne - The core mechanism of AlphaOne involves the introduction of a unified control point called α-moment, which dictates when to transition from slow to fast thinking [16][18]. - Prior to the α-moment, the model uses a probability-driven strategy to guide deep reasoning, while after the α-moment, it switches to a fast thinking mode [20][24]. Group 3: Experimental Results - In experiments across six reasoning tasks, AlphaOne demonstrated superior accuracy compared to existing models, with a notable increase of +6.15% in accuracy for a 1.5 billion parameter model [28][29]. - Despite employing a slow thinking mechanism, AlphaOne reduced the average number of generated tokens by 14%, showcasing its efficiency [30]. Group 4: Scalability and Flexibility - The α-moment allows for scalable adjustments to the thinking phase length, with the ability to increase or decrease the number of slow thinking markers based on the α value [34]. - The framework maintains robust performance across a wide range of α values, indicating its generalizability [34]. Group 5: Future Directions - The article suggests potential future research directions, including the development of more complex slow thinking scheduling strategies and the exploration of cross-modal reasoning applications [46][48].