Annually, NeurIPS generates thousands of excellent documents, and a handful that discreetly reset just how experts consider scaling, examination and system layout. In 2025, one of the most substantial jobs weren’t concerning a solitary innovation version. Rather, they tested essential presumptions that academicians and firms have actually silently relied upon: Larger versions indicate far better thinking …
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发布者：Hyunjee Ryu，转转请注明出处：https://robotalks.cn/why-reinforcement-learning-plateaus-without-representation-depth-and-other-key-takeaways-from-neurips-2025/
