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Self-Play Pretraining with Zero Data is introduced, an initial proof-of-concept towards realizing a more general approach to pretraining, and whether zero-shot performance on natural data improves predictably with self-play compute is tested.
Hierarchical correlations are a universal feature of any realistic model of data, and the question of how associative memory models may learn these correlations and generalize beyond them to construct new sensible images is an important step towards understanding more complex modern architectures such as diffusion mode...
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