benchmarks evals
A Model Practiced Prediction Without Natural Data
Generator programs supplied an open-ended self-play curriculum built from a universal Turing machine.
Summary
Generator programs supplied an open-ended self-play curriculum built from a universal Turing machine.
The proposed pretraining scheme pits a generator program against a learner that predicts the generator's output, using reinforcement learning to keep the curriculum challenging. The authors report that zero-shot loss on natural data scales with compute even though natural examples never enter pretraining. They also observe in-context learning and mathematical-sequence behavior, but this remains a controlled preprint result rather than a replacement recipe for production language-model training.
Why it matters
Generator programs supplied an open-ended self-play curriculum built from a universal Turing machine.
Limits and context
No additional limitation was separately recorded.
Key claims
Generator programs supplied an open-ended self-play curriculum built from a universal Turing machine.
Evidence: source-2026-09-27-011
Sources
- arXiv preprint 2609.30063arXiv · primary research
Corrections
No corrections have been recorded for this story.