Explorative Modeling: A Third Scaling Axis Beyond Data and Parameters
shangbinfeng · x · 2026-08-01
Researchers introduced 'Explorative Modeling,' a method that searches over multiple generations during training and learns from the best match, unlocking a third scaling axis for generative models beyond data and parameter size.
- Performance: Gains from exploration grow with scale, doubling at 3× compute.
- Efficiency: Adding exploration to near-SOTA baselines improves data efficiency by 6.2×, FLOP efficiency by 4.1×, and parameter efficiency by 47%, achieving a near-SOTA unguided FID of 1.43 on ImageNet.
- Mechanism: It allows trading training compute for improved generalization and scales end-to-end generation capabilities.
Related event: Explorative Modeling: Third Pretraining Axis, Up to 6x Sampling Efficiency(12 posts)→
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