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来源：小宇宙
我们都希望AI越来越聪明，但怎么才能让它高效成长呢？今天我们要聊的几篇最新论文，就给出了一些非常反直觉的答案：比如，让AI只做“难题”，给它的创作过程派一位“监理”，甚至还要警惕它因为懂得太多而“吃不饱”。更神奇的是，我们还会看到如何让AI学会新本事，却完全不忘旧手艺。准备好了吗？让我们一起看看AI是如何被调教成“学霸”的！
00:00:31 想让AI更聪明？你得学会给它出难题
00:05:53 如何让AI“心领神会”你的想法？
00:12:00 AI的“语义饱腹感”，为什么数据越多，进步越难？
00:18:31 AI思考的秘密，为什么“平行世界”里的笨办法，反而是捷径？
00:24:27 如何让AI学会新本事，还不忘了旧手艺？
本期介绍的几篇论文：
[CL] Scaling Data Difficulty: Improving Coding Models via Reinforcement Learning on Fresh and Challenging Problems
[Microsoft Research]
https://arxiv.org/abs/2603.07779
---
[LG] Diffusion Controller: Framework, Algorithms and Parameterization
[Google Research]
https://arxiv.org/abs/2603.06981
---
[LG] Scale Dependent Data Duplication
[Stanford University & EPFL]
https://arxiv.org/abs/2603.06603
---
[LG] Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference
[Microsoft Research & MIT]
https://arxiv.org/abs/2603.07887
---
[LG] Grow, Don't Overwrite: Fine-tuning Without Forgetting
[Google Research & University of Wisconsin-Madison]
https://arxiv.org/abs/2603.08647
00:00:31 想让AI更聪明？你得学会给它出难题
00:05:53 如何让AI“心领神会”你的想法？
00:12:00 AI的“语义饱腹感”，为什么数据越多，进步越难？
00:18:31 AI思考的秘密，为什么“平行世界”里的笨办法，反而是捷径？
00:24:27 如何让AI学会新本事，还不忘了旧手艺？
本期介绍的几篇论文：
[CL] Scaling Data Difficulty: Improving Coding Models via Reinforcement Learning on Fresh and Challenging Problems
[Microsoft Research]
https://arxiv.org/abs/2603.07779
---
[LG] Diffusion Controller: Framework, Algorithms and Parameterization
[Google Research]
https://arxiv.org/abs/2603.06981
---
[LG] Scale Dependent Data Duplication
[Stanford University & EPFL]
https://arxiv.org/abs/2603.06603
---
[LG] Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference
[Microsoft Research & MIT]
https://arxiv.org/abs/2603.07887
---
[LG] Grow, Don't Overwrite: Fine-tuning Without Forgetting
[Google Research & University of Wisconsin-Madison]
https://arxiv.org/abs/2603.08647
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