[人人能懂] 从不对称数据、自我审视到代码世界模型

今天我们来聊聊，怎样才能更聪明地培养一个AI，而不只是一味地堆砌数据和算力。我们会探讨，AI的“童年教育”怎样才能事半功倍？它又是如何学会像我们一样“先打草稿再修改”来提升工作效率的？从把AI变成程序员，到解开它“长考”反而犯错的谜团，再到给训练过程安装“涡轮增压”，最新几篇论文将刷新你对AI学习方式的认知。00:00:32 AI界的“鸡娃”指南00:05:12 AI写作提速：先打草稿，再一笔修正00:09:32 让AI下棋？不如让它当个“规则翻译官”00:14:52 AI“长考”之后，为什么反而会出错？00:20:56 AI训练的快车道：最后一层，我们算出来本期介绍的几篇论文：[LG] Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data  [NVIDIA & CMU]  https://arxiv.org/abs/2510.03264 ---[LG] Self-Speculative Masked Diffusions  [Google DeepMind]  https://arxiv.org/abs/2510.03929 ---[LG] Code World Models for General Game Playing  [Google DeepMind]  https://arxiv.org/abs/2510.04542 ---[LG] Understanding the Role of Training Data in Test-Time Scaling  [University of Southern California & University of California Los Angeles]  https://arxiv.org/abs/2510.03605 ---[LG] Closed-Form Last Layer Optimization  [Google Deep & Mind University of Tubingen & Secondmind]  https://arxiv.org/abs/2510.04606 

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