Skip the full retrain: small-scale runs plus a shared scaling law can tell whether a recommendation feature is worth shipping
Every feature change in a recommendation system normally needs a full-scale retrain for A/B testing, which is expensive, while a single small-scale run often gives a misleading signal. Wukong, HSTU and LLM-based recommenders are shown to follow the same functional form of scaling law after different features are added, differing only in fitted coefficients. Fitting that law to a handful of small-scale runs and applying statistical inference predicts, with confidence intervals, whether a feature helps, is neutral or regresses at full scale, using a fraction of full-scale compute.