Exploration as the Third Pretraining Axis: 6.2x Data Efficiency
teortaxesTex · x · 2026-08-02
Research proposes 'exploration' as a third pretraining axis beyond parameters and data. Scaling exploration monotonically improves existing models across images, video, and language, unlocking end-to-end generation through a simple for-loop.
Gains grow with scale: data efficiency improves by 6.2x, FLOP efficiency by 4.1x, and parameter efficiency by 47%. It also achieves a near-SOTA unguided FID of 1.43 on ImageNet, allowing researchers to trade training compute for generalization.
Related event: Explorative Modeling: Third Pretraining Axis, Up to 6x Sampling Efficiency(12 posts)→
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