arXiv AI September 25, 2026 By Aditya Cowsik, Kfir Dolev, Michael Y. Li, G. Bruno De Luca, Nourya Cohen, Noah D. Goodman, Yoav Levine

Self-Play Pretraining with Zero Data

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The paper introduces Self‑Play Pretraining with Zero Data, a proof‑of‑concept method that lets a model generate its own training data by searching over all computable processes using a universal Turing machine. Two models— a generator that proposes byte‑sequence programs and a learner that predicts those sequences—train together, with the generator rewarded for producing data at the learner’s frontier, creating an adaptive curriculum. Experiments show that zero‑shot performance on natural datasets scales predictably with compute, and the models exhibit in‑context learning and discover mathematical sequences during training.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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