[人人能懂] AI的“手艺人”精神：从模仿、练习到顿悟

00:01:33 “差生”配对，如何“炼”出优等生？ 

00:06:09 AI的“刻意练习”：怎样探索才最高效？ 

00:10:19 让AI学会顶尖“手艺活”，这事儿靠谱吗？ 

00:14:35 黑箱里的光：我们好像找到了AI学习的秘密开关 

00:19:47 AI 程序员的“心事”：它真的懂你的需求吗？ 

今天介绍的五论文：
[LG] The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains

[University of Washington]

https://arxiv.org/abs/2507.06187

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[LG] Epistemically-guided forward-backward exploration

[ETH Zurich & University of Tübingen]

https://arxiv.org/abs/2507.05477

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[LG] AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs

[Tsinghua University]

https://arxiv.org/abs/2507.05687

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[LG] FACT: the Features At Convergence Theorem for neural networks

[MIT & UCSD & UC Berkeley]

https://arxiv.org/abs/2507.05644

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[CL] Coding Triangle: How Does Large Language Model Understand Code?

[Shanghai AI Laboratory]

https://arxiv.org/abs/2507.06138