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

Is AI Taste Just a Longer Context Window?

Charlie O'Neill asks whether research taste is just a longer context window. Give a model a trillion tokens, enough to hold a person's experience before RLHF, with the same sample efficiency it already shows at a million tokens, and ask if taste is solved.

John Schulman says no. The model would still have to be trained to learn the right update from that context, or the skill would have to generalize. A life cannot simply be dumped in.

Beren Millidge says a trillion-token window would still need trillion-length data to train, and a 10k context cannot hold that dump. Humans do not live to be 10,000 and still pick up taste quickly. The difference between a first-year PhD student and a final postdoc is about five years, across maybe 10 to 30 research projects. Taste shows up from that short succession, so the episodes do not have to be extremely long. An AI would have vastly more experience in which to develop taste. Whether that learning reaches long-horizon judgments is unsolved.

Transcript

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

Dwarkesh Podcast

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