Explorative Modeling: Discovering the Third Pretraining Axis Beyond Parameters and Data
YouJiacheng · x · 2026-08-02
Alexi Glad and colleagues introduced Explorative Modeling, revealing a third pretraining axis beyond parameters and data: exploration.
- Core Findings: Scaling exploration monotonically improves existing models. The gains grow with data, parameters, and compute scale.
- Efficiency Boost: Adding exploration to near-SOTA baselines improves data efficiency by 6.2x, FLOP efficiency by 4.1x, and parameter efficiency by 47%, achieving a near-SOTA 1.43 unguided FID on ImageNet.
- Technical Insight: Exploration trades training compute for generalization and scales end-to-end generation. Zhengyang Geng noted this aligns with putting richer computation into each update (like training-time recurrence), following the principle of "train harder, sample easier."
Related event: Explorative Modeling: Third Pretraining Axis, Up to 6x Sampling Efficiency(12 posts)→
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