1000-Layer Networks for Self-Supervised RL

NeurIPS 2025 Best Paper (Eysenbach's group, with Warsaw University of Technology): scaling network depth to ~1000 layers in contrastive self-supervised RL unlocks qualitatively new goal-reaching, with 2–50× performance gains — a depth-scaling result for RL analogous to width/data scaling for LLMs.