想知道如何把临时指令“刻”进AI的大脑，让它拥有真正的肌肉记忆吗？我们又该如何教AI学会“抄近道”，一步生成作品，而不是慢慢搭建？本期节目，我们将深入最新论文，探讨如何让AI不仅做对事，更要想对事，并揭示在调教AI时，那些我们习以为常却可能导致它“偏执”或“精神分裂”的惊人误区。

00:00:28 AI的“肌肉记忆”是怎么炼成的？

00:05:48 造物，如何抄近道？

00:11:04 AI调教指南，你以为的，不是你以为的

00:17:32 比做对事更重要的，是想对事

00:22:45 AI调教指南，为什么你喂得越多，它可能变得越偏执

本期介绍的几篇论文：

[CL] On-Policy Context Distillation for Language Models

[Microsoft Research]

https://arxiv.org/abs/2602.12275

---

[LG] Categorical Flow Maps

[University of Amsterdam & University of Oxford]

https://arxiv.org/abs/2602.12233

---

[LG] The Magic Correlations: Understanding Knowledge Transfer from Pretraining to Supervised Fine-Tuning

[Google DeepMind & Google Research]

https://arxiv.org/abs/2602.11217

---

[LG] Right for the Wrong Reasons: Epistemic Regret Minimization for Causal Rung Collapse in LLMs

[Stanford University]

https://arxiv.org/abs/2602.11675

---

[LG] How Sampling Shapes LLM Alignment: From One-Shot Optima to Iterative Dynamics

[PSL Research University & Northwestern University]

https://arxiv.org/abs/2602.12180