Meta paper uses scaling laws to predict full-scale A/B results from small runs
_reachsumit · x · 2026-11-13
Meta published a paper showing how feature-invariant scaling laws can predict full-scale A/B experiment outcomes for recommendation model changes from just a few small-scale runs.
By identifying feature statistics whose scaling behavior is stable between small and full-scale training, the method extrapolates real production impact without running massive online experiments - a direct lever on iteration speed and experimentation cost for recsys teams.
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