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节目简介
来源：小宇宙
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
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
