[人人能懂] 从少食多餐、应对打断到循环自救

你有没有想过，让AI变得更聪明，究竟是该让它“一口吃成胖子”，还是鼓励它“想得不一样”？当我们打断一个正在思考的AI，它会惊慌失措吗？而它从模仿到思考的关键飞跃，背后又藏着怎样的秘密？面对即将到来的数据“粮食危机”，AI又将如何自救？本期节目，我们就从五篇最新论文出发，一起探寻AI学习与思考的底层逻辑。

00:00:32 从“一口吃成胖子”到“少食多餐”：AI学习的新智慧

00:06:22 AI正在“思考”，这时你打断它会发生什么？

00:10:56 AI的“粮食危机”，靠“循环农业”能解决吗？

00:16:04 让AI大模型“开窍”的秘密：不止要“刷对题”，更要“想不同”

00:21:06 从“傻瓜式”模仿到“聪明地”思考，AI只差这关键一步

本期介绍的几篇论文：

[LG] Iterative Amortized Inference: Unifying In-Context Learning and Learned Optimizers

[Mila]

https://arxiv.org/abs/2510.11471

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[CL] Are Large Reasoning Models Interruptible?

[UC Berkeley]

https://arxiv.org/abs/2510.11713

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[CL] RePro: Training Language Models to Faithfully Recycle the Web for Pretraining

[CMU]

https://arxiv.org/abs/2510.10681

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[LG] Representation-Based Exploration for Language Models: From Test-Time to Post-Training

[Microsoft Research NYC & Princeton University]

https://arxiv.org/abs/2510.11686

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[LG] How Reinforcement Learning After Next-Token Prediction Facilitates Learning

[New York University & Harvard University & Meta]

https://arxiv.org/abs/2510.11495