What does the next training paradigm look like?

Dwarkesh Patel explores the current AI training paradigm, focusing on the "big research bet" on scaling RL in verifiable environments. He critiques its limitations in generalizing to real-world, non-grindable tasks and the inefficiency of current inference, advocating for advanced continual learning techniques like On-Policy Self-Distillation and "dreaming" to enable AIs to learn on the job and improve through broad deployment.