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Facebook Engineering
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From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking
Meta's engineering team presents two architectural breakthroughs for ads ranking at scale: a multi-stage sequence model that decouples heavy offline user modeling from lightweight online ranking, and a dense tokenization with target-aware attention technique. The offline stage processes thousands of user events asynchronously using deep transformers, caching embeddings that the online stage combines with real-time signals. This separation allows model complexity to scale without proportional serving cost increases. Notably, the system exhibits LLM-style log-linear scaling laws despite working with sparse ID features rather than dense text. Four scaling levers are identified: balanced model shape, multi-stage tunability, sequence diversity, and semantic feature enrichment. The architecture, a core part of Meta's GEM platform, has driven cumulative lifts of 6% in Instagram conversions, 3% in Facebook conversions, and 3.5% in Facebook ad clicks.
Source: https://engineering.fb.com/2026/08/05/ml-applications/from-user-sequences-to-scaling-laws-a-multi-stage-architecture-for-metas-ads-ranking. 8sync News only summarizes and links out; content copyright belongs to the authors and original sources.
