我们总觉得AI越大越好，但如果一个AI能像大公司一样知识渊博，却只用一个小团队的成本来思考，是不是更酷？本期节目，我们就从几篇最新论文出发，看看AI如何学会当一个聪明的“调度员”，如何像学徒一样承认“不确定性”来学得更快，甚至如何通过“复盘”和“划重点”来真正实现“吃一堑、长一智”。准备好，一起探索AI更聪明、更高效的进化之路吧！00:00:33 AI大模型的小秘密：如何用一个“小团队”，干翻一个“大公司”？00:05:55 聪明的“笨功夫”：如何让机器人学得更快？00:12:08 让AI学会“吃一堑、长一智”，需要几步？00:17:27 AI的“七秒记忆”难题，如何用“划重点”来解决？00:23:06 机器人学徒：如何从“笨拙模仿”到“青出于蓝”？本文介绍的几篇论文：[CL] Sigma-Moe-Tiny Technical Report[Microsoft Research]https://arxiv.org/abs/2512.16248---[LG] Posterior Behavioral Cloning: Pretraining BC Policies for Efficient RL Finetuning[UC Berkeley & Stanford]https://arxiv.org/abs/2512.16911---[LG] Meta-RL Induces Exploration in Language Agents[EPFL & Idiap Research Institute]https://arxiv.org/abs/2512.16848---[LG] Kascade: A Practical Sparse Attention Method for Long-Context LLM Inference[Microsoft Research India]https://arxiv.org/abs/2512.16391---[RO] ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning[University of Toronto & Georgia Institute of Technology & NVIDIA Research]https://arxiv.org/abs/2512.16861

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

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