AI可可AI生活

[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