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

The NeurIPS 2025 conference highlighted a shift where AI progress is constrained less by raw model size and more by system design, architecture, training dynamics, and evaluation strategies. Key findings demonstrate that language models increasingly converge on homogeneous outputs in creative tasks,