你有没有想过，让AI变聪明，或许并不需要更强的算力，而是需要一种更巧妙的引导方式？本期，我们将一起探索几篇最新论文带来的奇妙洞见：我们会发现，一点点“计算噪声”竟能让AI学得更好；我们甚至能像做CT扫描一样，亲眼“看见”AI思考的几何轨迹；并学习如何像教育孩子一样，教会AI在探索与专注间找到完美平衡，甚至不花一分钱，就解锁它的隐藏潜能。

00:00:36 不花钱升级你的AI？换个提问方式就行

00:05:39 AI育儿经：如何教机器学会“恰到好处”的探索

00:11:50 训练AI，加点“噪声”效果更好？

00:16:47 AI的“心流”：看见思考的轨迹

00:22:19 如何让聪明的AI，学会更聪明地做事？

本期介绍的几篇论文：

[LG] Reasoning with Sampling: Your Base Model is Smarter Than You Think

[Harvard University]

https://arxiv.org/abs/2510.14901

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[LG] Agentic Entropy-Balanced Policy Optimization

[Kuaishou Technology & Renmin University of China]

https://arxiv.org/abs/2510.14545

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[LG] QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs

[NVIDIA & MIT]

https://arxiv.org/abs/2510.11696

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[LG] The Geometry of Reasoning: Flowing Logics in Representation Space

[Duke University]

https://arxiv.org/abs/2510.09782

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[CL] Demystifying Reinforcement Learning in Agentic Reasoning

[National University of Singapore & Princeton University & University of Illinois at Urbana-Champaign]

https://arxiv.org/abs/2510.11701