Core Insights - The development of large AI models is evolving towards enabling deeper thinking capabilities while addressing the issue of "overthinking" in simpler tasks [1][2] - The introduction of the AutoThink strategy allows models to autonomously switch thinking modes based on the difficulty of the problem, enhancing efficiency and accuracy [2] Group 1: AutoThink Strategy - AutoThink employs ellipsis prompts combined with a three-stage reinforcement learning approach to guide large models in deciding whether to think deeply or not based on problem difficulty [2] - This strategy has shown a balance between accuracy and efficiency across multiple mathematical datasets, improving performance while conserving computational resources [2] Group 2: Integration and Future Directions - AutoThink has been integrated into the one-stop intelligent research platform ScienceOne and will be used to train the foundational model S1-Base [2] - The development team emphasizes that making large models "think smarter and express more concisely" is a crucial direction for the evolution of foundational scientific models [2]
【新华社】我国科学家提出高效推理策略 可避免大模型“过度思考”
Xin Hua She·2025-05-30 00:34