Dwarkesh Patel: The Next Generation of AI May Be Built Through Actual Work
In his latest podcast, Dwarkesh Patel explores the next paradigm for AI training. While current progress in fields like coding and math relies on Reinforcement Learning with Verifiable Rewards (RLVR), which requires tasks that are both verifiable and highly scalable ("grindable"), Patel questions whether this is sufficient for complex real-world objectives like starting a business, winning a legal case, or managing an organization. These tasks provide verifiable outcomes but lack the resetable, parallelizable environments needed for efficient RLVR training.
Patel argues the key limitation of current models is their inability to convert valuable in-context learning from real deployment into permanent weight updates—a process he terms "learning back to the weights." He proposes two potential solutions: On-Policy Self-Distillation (OPSD), where a model distills knowledge from long, task-specific sessions back into its base weights, and "dreaming," where an AI constructs simulated environments from real-world observations to practice and refine strategies.
Ultimately, Patel envisions a future training paradigm where AI advances not just through pre-training on static datasets but through continual, post-deployment learning from real-world experience. This shift would enable AI to move beyond "grindable" tasks and develop robust, generalizable agent capabilities for complex, real-world challenges.
marsbit06/28 23:49
