Scaling RL with Verifiable Rewards Hits Wall Without Continual Learning
AI labs are betting that scaling reinforcement learning with verifiable rewards across millions of tasks will yield AGI, but the approach stalls where deterministic simulators are unavailable. The missing piece is true continual learning—updating model weights from real‑world deployment—not just longer context windows.