Massive compute per sample could let models "ruminate" to insights, argues moultano
moultano · x · 2026-10-05
moultano sketches a training idea: with a very high ratio of compute to samples, you could take every surprising sample and run vast numbers of world-model rollouts to find one that reproduces the surprise, then update on it — effectively letting a model "ruminate until it has an insight." A concise speculation linking test-time compute to learning.
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