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

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 strategy.

Below is a technical deep dive into five of the most influential NeurIPS 2025 papers — and what they mean for anyone building real-world AI systems.

1. LLMs are converging—and we finally have a way to measure it

Paper: Artificial Hivemind: The Open-Ended Homogeneity of Language Models

For years, LLM evaluation has focused on ...