New paper: tweaking recursive depth improves pre-training scaling exponents at scale
bo_wangbo · x · 2026-09-17
A new paper from Andrew G. Wilson's group shows that modifying recursive depth (looping) for model growth can improve scaling exponents in pre-training — compute-efficiency gains that increase with scale. It suggests architecture-level changes can beat simply pouring in more data and parameters.
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