Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)
via venturebeat.com
Short excerpt below. Read at the original source.
Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works weren’t about a single breakthrough model. Instead, they challenged fundamental assumptions that academicians and corporations have quietly relied on: Bigger models mean better reasoning, RL creates […]