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

Can a Little Reinforcement Learning Change an AI Model?

Beren Millidge argues that a little reinforcement learning can still change an AI model in a dramatic way. Even one bit can shift the input-to-output mapping a lot and rule out half the hypothesis space, so a small parameter update from a strong starting point can produce a large change in behavior.

Charlie O'Neill says math training does not necessarily make a model the best coder, and code still needs its own reinforcement learning environments. What did generalize is the horizon. Models learned to spend more tokens, keep working longer, and still make progress. A paper called EdgeBench showed that the rate at which models can work for longer is doubling every three months. He compares the smooth pre-training loss to tens of thousands or millions of discrete skills switching on at separate moments and averaging into one curve. A single finance or Excel task can jump from a 0.5% pass rate to a 90% pass rate. Across many tasks, plus the longer horizon, the models look qualitatively better.

Beren Millidge adds that reinforcement learning also transfers a bit between math, code, and puzzles, and that labs now build far more environments for ordinary daily tasks than they did two years ago.

Transcript

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

Dwarkesh Podcast

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