Beyond Bigger Models: Recursion As The Next Scaling Law In AI

Recent advancements with Hierarchical Reasoning Models (HRM) and Tiny Recursive Models (TRM) show how recursion at inference time enables small, 7-million parameter models to outperform models 1000x their size on complex reasoning tasks. This is achieved by giving models compute depth to break through the inherent reasoning ceilings of standard feed-forward Transformers.