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节目简介
来源：小宇宙
你有没有想过，让人工智能变聪明的秘诀，可能不是“更多”，而是“更巧”？本期我们要聊的几篇最新论文，就充满了这种“反常识”的智慧：从把效率从细节里“省”出来，到警惕信息太丰富反而让AI“变笨”的悖论。我们还会看到，一个“完美”的陪练为何会带出最差的学生，以及如何通过精心呵护AI的“童年”，来预测它未来的潜力。准备好，让我们一起在这些看似矛盾的发现中，窥见AI的未来。
00:00:32 省出来的效率，才是真本事
00:07:29 AI的“富贵病”，为什么信息越多，它反而越“笨”？
00:11:44 你的AI陪练，正在让你变傻
00:17:46 人工智能的“童年”里，藏着未来的密码
00:22:55 AI瘦身术，从“一刀切”到“看人下菜”的智慧
本期介绍的几篇论文：
[LG] Dion3: Full-Stack Orthogonal Updates
[New York University & Princeton University & NVIDIA]
https://arxiv.org/abs/2608.11612
---
[CL] Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge
[Johns Hopkins University]
https://arxiv.org/abs/2608.12218
---
[CL] One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL
[Northeastern University & New York University & UC Berkeley]
https://arxiv.org/abs/2608.12253
---
[LG] Small-Scale Experiments: Are We There Yet?
[FAIR at MSL Meta & New York University]
https://arxiv.org/abs/2608.11859
---
[LG] SoftWater: Class-Aware Rate Allocation for Softmax Quantization
[MIT]
https://arxiv.org/abs/2608.12026
00:00:32 省出来的效率，才是真本事
00:07:29 AI的“富贵病”，为什么信息越多，它反而越“笨”？
00:11:44 你的AI陪练，正在让你变傻
00:17:46 人工智能的“童年”里，藏着未来的密码
00:22:55 AI瘦身术，从“一刀切”到“看人下菜”的智慧
本期介绍的几篇论文：
[LG] Dion3: Full-Stack Orthogonal Updates
[New York University & Princeton University & NVIDIA]
https://arxiv.org/abs/2608.11612
---
[CL] Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge
[Johns Hopkins University]
https://arxiv.org/abs/2608.12218
---
[CL] One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL
[Northeastern University & New York University & UC Berkeley]
https://arxiv.org/abs/2608.12253
---
[LG] Small-Scale Experiments: Are We There Yet?
[FAIR at MSL Meta & New York University]
https://arxiv.org/abs/2608.11859
---
[LG] SoftWater: Class-Aware Rate Allocation for Softmax Quantization
[MIT]
https://arxiv.org/abs/2608.12026
外观
