Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)
FeaturedMaitreyi Chatterjee,Devansh Agarwal January 17, 2026 CleoP made with MidjourneyImage generated using OpenAI’s DALL·E 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 new capabilities, attention is “solved” and generative models inevitably memorize.This year’s top papers collectively point to a deeper shift: AI progress is now constrained less by raw model capacity and more by architecture, training dynamics and evaluation…
