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

DevFeed: [From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking](<https://devfeed.tech/articles/from-user-sequences-to-scaling-laws-a-multi-stage-architecture-for-meta-s-ads-ranking-128.md>)

Original publisher: [Read original article](<https://engineering.fb.com/2026/08/05/ml-applications/from-user-sequences-to-scaling-laws-a-multi-stage-architecture-for-metas-ads-ranking/>)

Author: Steven De Gryze; Parshva Doshi; Sean O'Byrne; Arnold Overwijk; Dinesh Ramasamy; Lee Xiong

Published: 2026-08-05T19:20:20Z

Content type: article

Language: en

Sources: [Engineering at Meta](<https://devfeed.tech/sources/engineering-at-meta.md>), [Meta ML Applications](<https://devfeed.tech/sources/meta-ml-applications.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Temporal data](<https://devfeed.tech/topics/temporal-data.md>)

Tags: [ads](<https://devfeed.tech/tags/ads.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [data-infrastructure](<https://devfeed.tech/tags/data-infrastructure.md>), [meta](<https://devfeed.tech/tags/meta.md>), [ml-applications](<https://devfeed.tech/tags/ml-applications.md>), [production-engineering](<https://devfeed.tech/tags/production-engineering.md>), [recommendation-systems](<https://devfeed.tech/tags/recommendation-systems.md>), [scaling-laws](<https://devfeed.tech/tags/scaling-laws.md>), [tokenization](<https://devfeed.tech/tags/tokenization.md>)

## AI overview

Meta describes a multi-stage sequence-model architecture for ads ranking that separates offline user modeling from lightweight online ranking. It also uses dense tokenization and target-aware attention to learn feature interactions, with reported conversion and ad-click lifts across Instagram and Facebook.

## Source excerpt

Every day, Meta's recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) [...] Read More... The post From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking appeared first on Engineering at Meta.