今天我们不聊模型参数有多大，而是聊如何让AI变得更“会思考”，这种思考方式，有时甚至有些反常识。比如，为什么给AI疯狂“补课”，它反而可能越学越笨？我们还会探讨，如何像一位高明的老师一样引导AI攻克难题，而不是直接灌输答案。更进一步，我们会揭示如何训练AI像个侦探一样，学会“讲道理”地分析代码，以及如何让整个系统学会动态协作，找到最高效的“偷懒”方式。00:00:35 AI大模型时代，如何花小钱办大事？00:05:47 给AI“补课”的陷阱，为什么学得越多，它反而越笨？00:11:37 高手辅导功课，为什么不直接给答案？00:16:48 让AI学会“讲道理”，代码世界的侦探是怎样炼成的？00:22:00 让AI学会“省时间”，一种更聪明的快本期介绍的几篇论文：[LG] Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization [Google DeepMind & University of Michigan] https://arxiv.org/abs/2603.02029 ---[LG] Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models [University of Southern California & University of California Los Angeles & Google Research] https://arxiv.org/abs/2603.01293 ---[LG] Learn Hard Problems During RL with Reference Guided Fine-tuning [ByteDance Seed & UC Berkeley & CMU] https://arxiv.org/abs/2603.01223 ---[LG] Agentic Code Reasoning [Meta] https://arxiv.org/abs/2603.01896 ---[CL] Learning to Draft: Adaptive Speculative Decoding with Reinforcement Learning [Microsoft Research Asia & Peking University] https://arxiv.org/abs/2603.01639

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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