Explorative Modeling: A Third Pretraining Axis Boosting Efficiency Up to 6.2×
_akhaliq · x · 2026-08-01
The paper introduces Explorative Modeling, a new generative modeling paradigm that acts as a third pretraining axis for existing models and enables end-to-end generation.
Experiments show that increasing exploration monotonically improves performance across images, video, and language, with gains scaling alongside data (7%→36%) and parameters (13%→23%).
Concretely, Explorative Models (XMs) achieve 6.2× sample efficiency, 4.1× FLOP efficiency, and 47% better parameter efficiency. On control tasks, XMs match diffusion models while requiring up to 256× less inference compute.
Related event: Explorative Modeling: Third Pretraining Axis, Up to 6x Sampling Efficiency(12 posts)→
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