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Back to AI researchers debate how close we are to recursive self-improvement, with John Schulman & Beren Millidge.

Why Does Distillation Fight AI Model Consolidation?

John Schulman says distillation is the main thing that fights the centralizing force among AI model providers. Anything that can be learned through reinforcement learning is a small number of bits and can be learned from a small amount of data, so it distills very easily. Trajectories that show a behavior are enough to distill that behavior.

Company-specific models remain possible. A company could learn from deployment and keep improving its own model. That system could come from the current oligopoly of model providers or from a smaller company, and he thinks that would change the game. Beren Millidge adds that continual learning does not stop distillation. Even if a model improves every day, people could distill it every day, and the two loops could run at the same pace.

Transcript

AI researchers debate how close we are to recursive self-improvement ·

Dwarkesh Podcast

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