[NeurIPS Best Paper] 1000 Layer Networks for Self-Supervised RL — Kevin Wang et al, Princeton
Dec 31, 2025 · 28:19
Kevin Wang, Ishaan Javali, Michał Bortkiewicz, and Benjamin Eysenbach won the NeurIPS 2025 Best Paper award for RL1000, defying conventional wisdom by scaling reinforcement learning networks to 1,000 layers deep. They show that self-supervised RL—learning representations of states, actions, and future states via contrastive classification—scales where value-based methods collapse, and that critical architectural tricks (residual connections, layer norm, classification instead of regression) unlock performance that multiplies beyond a critical depth. Scaling depth is more parameter-efficient (linear growth) than width (quadratic growth), and Jax GPU-accelerated environments enable collecting hundreds of millions of transitions in hours, providing the data abundance that makes scaling possible. The paper argues that RL is finally ready to scale like language and vision, not by throwing compute at value functions, but by borrowing self-supervised objectives, with implications for robotics via goal-conditioned RL without human supervision or demonstrations, and deployment via deep teacher, shallow student distillation.
