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

How Does Cursor Ship a New Composer Model?

Charlie O'Neill says Cursor's online reinforcement learning for Composer has no groups. One user says one thing and the team gets one rollout, which creates a variance problem. Cursor's answer was heuristics that estimate how much better or worse than average a response was, then a large REINFORCE update. If the new model improved on CursorBench, they deployed it every five hours. If it did not, they threw that version out.

The older Tab model already learned every day from whether people pressed Tab on the next completion. Composer then did the same thing for the generative model. A person still chooses the signals and builds environments from deployment data, so the loop is slower than a model updating on the spot. O'Neill expects that loop to get faster. John Schulman adds that the harder problem is not knowing the reward from natural data. A superficial signal, such as whether someone accepted an edit, can be reward-hacked.

Grokipedia·Cursor·Composer

Transcript

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

Dwarkesh Podcast

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