Zero-data pretraining: self-play models learn ICL without any real data
burny_tech · x · 2026-09-27
A proof-of-concept paper on Self-Play Pretraining with Zero Data: two models start from random init — a generator proposes programs for a universal Turing machine, a learner trains on their outputs, never touching real data. Zero-shot val loss on images, text, audio and melodies decreases predictably with self-play compute, and the learner develops in-context learning. Commenters note it supports the universality hypothesis: analogous features and circuits emerge across networks regardless of data. Co-led with Noah Goodman and Yoav Levine among collaborators.
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