From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking

This post introduces a multi-stage architecture for Meta's ads ranking system that decouples heavy offline user modeling from lightweight online ranking tasks. It also details a learning technique based on dense tokenization and target-aware attention for efficient feature interaction learning. The architecture enables LLM-style scaling laws for ads recommendations, with performance improvements following a log-linear relationship with compute. Key scaling levers identified are balanced model shape, multi-stage tunability, sequence composition (diversity over homogeneity), and semantic feature representation. This system has contributed to a cumulative lift of 6% in conversions on Instagram, 3% in conversions on Facebook, and 3.5% in ad clicks on Facebook. It is integrated into Meta's Generative Ads Recommendation Model (GEM).