[人人能懂] 从“笨办法”学习到“随身小抄”

今天我们不聊模型又变大了多少，而是聊几个让AI变得更聪明、更高效的“巧思”。我们会看到，AI如何用“笨办法”打破人类专家的优化极限，又为什么一本精心准备的“错题本”却教不会它自我反思。接着，我们会探索如何用“名师点拨”和“随身小抄”让AI低成本地自我进化。最后，看看如何让AI裁判学会投出更“聪明”的一票，而不仅仅是少数服从多数。准备好了吗？让我们一起看看，这些最新论文是如何用“四两拨千斤”的智慧，刷新我们对人工智能的认知。00:00:40 人工智能时代，还有“最优解”这回事吗？00:05:11 给AI上“错题本”，它就能学聪明吗？00:09:37 AI自学的终极秘诀：不是“题海战术”，而是“名师点拨”00:13:43 AI太贵用不起？这里有个“随身小抄”的省钱妙计00:20:13 AI当裁判，如何投出更聪明的一票？本期介绍的几篇论文：[LG] CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning[DeepReinforce Team]https://arxiv.org/abs/2512.02551---[LG] Synthetic Error Injection Fails to Elicit Self-Correction In Language Models[UC Berkeley]https://arxiv.org/abs/2512.02389---[LG] Guided Self-Evolving LLMs with Minimal Human Supervision[Tencent AI Lab in Seattle & Washington University in St. Louis]https://arxiv.org/abs/2512.02472---[LG] In-Context Distillation with Self-Consistency Cascades: A Simple, Training-Free Way to Reduce LLM Agent Costs[Stanford University & Reve]https://arxiv.org/abs/2512.02543---[LG] Distribution-Calibrated Inference time compute for Thinking LLM-as-a-Judge[Google & Google DeepMind]https://arxiv.org/abs/2512.03019

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