[人人能懂] 从失败步骤、异步流程到多路径融合

我们总觉得AI变聪明，就是靠更多数据和更强算力，但今天我们要聊的几篇最新论文，揭示了另一条更聪明的捷径。我们将看到，顶尖的AI如何学会避免“走弯路”来提升思考质量，又如何像一个高效的项目经理，果断“叫停”慢任务，不再傻等。接着，我们会探索AI如何用一种“模糊”的艺术进行训练，像一个内部“诸葛亮会”一样进行多角度的头脑风暴，甚至变身“程序员”自己写代码来解决问题。这些研究不仅在优化AI，更是在颠覆我们对“高效思考”的理解，准备好一起脑力升级了吗？00:00:43 AI思考的秘密：走弯路，原来这么“致命”？00:06:22 AI效率革命：不等那个“最慢的同学”00:11:34 AI思考的“模糊”艺术00:17:02 AI的“分身术”：高手解决问题，靠的不是一条路走到黑00:22:26 高手AI，不靠“背书”，靠“编程”本期介绍的几篇论文：[LG] What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT  [Meta Superintelligence Labs]  https://arxiv.org/abs/2509.19284  ---[LG] APRIL: Active Partial Rollouts in Reinforcement Learning to tame long-tail generation  [Advanced Micro Devices, Inc. (AMD) & Carnegie Mellon University (CMU)]  https://arxiv.org/abs/2509.18521  ---[CL] Soft Tokens, Hard Truths  [University of Amsterdam]  https://arxiv.org/abs/2509.19170  ---[CL] Pathways of Thoughts: Multi-Directional Thinking for Long-form Personalized Question Answering  [University of Massachusetts Amherst & Google DeepMind]  https://arxiv.org/abs/2509.19094  ---[CL] Actions Speak Louder than Prompts: A Large-Scale Study of LLMs for Graph Inference  [Microsoft Research & University of Oxford]  https://arxiv.org/abs/2509.18487  

There are no comments yet.

Please log in to write the first comment.