[人人能懂] 智能修炼手册：从AI前沿看穿成长与学习的底层逻辑

00:01:36 你的勤奋，是在“拼宽度”还是在“拼深度”？00:06:00 AI 变聪明的秘密：不止是“喂饱”，更要“喂好”00:11:19 AI的“学霸修炼手册”：如何跳出成长平台期？00:16:15 给AI一个“提示”，解锁更高阶的智慧00:20:23 你怎么用“临时工”大脑，摆平全世界？00:05:30 喂AI，也是一门技术活今天介绍的六篇论文：[LG] The Serial Scaling Hypothesis  [UC Berkeley]  https://arxiv.org/abs/2507.125  ---[LG] Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)  C Qin, J T Springenberg  https://arxiv.org/abs/2507.12856  ---[LG] Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training  M Liu, S Diao, J Hu, X Lu...  [NVIDIA]  https://arxiv.org/abs/2507.12507  ---[CL] QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation  J Li, H Lu, K Wen, Z Yang...  [Ant Research & Shanghai Qi Zhi Institute & Stanford University & Tsinghua University]  https://arxiv.org/abs/2507.13266  ---[CL] Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models  L Wong, K M. Collins, L Ying, C E. Zhang...  [Stanford University & MIT]  https://arxiv.org/abs/2507.12547  ---[CL] A Survey of Context Engineering for Large Language Models  https://arxiv.org/abs/2507.13334  

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