AI的自我进化，听起来很酷，但最新论文告诉我们，AI学徒也需要一位聪明的“教练”为它精心设计训练计划，否则刷再多题也难成大器。我们还会揭示一个奇怪的现象：为什么让AI向完美的自己“抄作业”，反而可能让它在关键的推理任务上变笨？而在使用AI时，你是否发现它总“忘事”，或者那个标价最便宜的模型，最后反而让你花了最多的钱？今天，我们就从五篇最新论文出发，聊聊AI那些出人意料的“成长烦恼”和“使用陷阱”。00:00:38 AI“学徒”的成长烦恼，为什么聪明的大模型也需要好师傅？00:06:54 聪明反被聪明误，为什么教AI“抄作业”反而会让它变笨？00:12:11 你的“私人教练”，不该只会题海战术00:18:11 你以为的便宜，可能让你花得更多00:23:43 你的AI“听话”吗？小心它忙起来就忘了本期介绍的几篇论文：[LG] Understanding the Challenges in Iterative Generative Optimization with LLMs[CNRS & Stanford University & CMU]https://arxiv.org/abs/2603.23994---[CL] Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?[Microsoft Research & Seoul National University]https://arxiv.org/abs/2603.24472---[LG] A Deep Dive into Scaling RL for Code Generation with Synthetic Data and Curricula[Meta FAIR & University of Tübingen]https://arxiv.org/abs/2603.24202---[LG] The Price Reversal Phenomenon: When Cheaper Reasoning Models End Up Costing More[Stanford University & UC Berkeley & CMU]https://arxiv.org/abs/2603.23971---[CL] Did You Forget What I Asked? Prospective Memory Failures in Large Language Models[Microsoft]https://arxiv.org/abs/2603.23530

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

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