Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)

Source: VentureBeat

Overview

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 academia and industry have long taken for granted, pushing the field toward deeper, more robust approaches.