How Meta Cut Its Ads Model Serving Cost From 24.6x to 3.9x (HILL, Explained)
Summary
Meta published the first systematic teardown of MoNN, the retrieval model behind Facebook and Instagram ads, plus HILL, the hierarchical index that made it affordable. The big model wins +9.4% recall at 24.6x serving cost; organizing every ad into a learned tree keeps +6.0% at 3.9x, and the production A/B moved online metrics +2.57%. Bonus finding: the tree's middle layers double as training sets for test-time fine-tuning. Paper: arxiv.org/abs/2604.12965
About this video
Meta published the first systematic teardown of MoNN, the retrieval model behind Facebook and Instagram ads, plus HILL, the hierarchical index that made it affordable. The big model wins +9.4% recall at 24.6x serving cost; organizing every ad into a learned tree keeps +6.0% at 3.9x, and the production A/B moved online metrics +2.57%. Bonus finding: the tree's middle layers double as training sets for test-time fine-tuning. Paper: arxiv.org/abs/2604.12965