LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation

Lee Xiong; Zhirong Chen; Rahul Mayuranath; Shangran Qiu; Arda Ozdemir; Lu Li; Yang Hu; Dave Li; Jingtao Ren; Howard Cheng; Fabian Souto Herrera; Ahmed Agiza; Baruch Epshtein; Anuj Aggarwal; Julia Ulziisaikhan; Chao Wang; Dinesh Ramasamy; Parshva Doshi; Sri Reddy; Arnold Overwijk

arXiv · 2601.20083

LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation

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Abstract

We present LLaTTE (LLM-Style Latent Transformers for Temporal Events), a scalable transformer architecture for production ads recommendation. Through systematic experiments, we demonstrate that sequence modeling in recommendation systems follows predictable power-law scaling similar to LLMs. Crucially, we find that semantic features bend the scaling curve: they are a prerequisite for scaling, enabling the model to effectively utilize the capacity of deeper and longer architectures. To realize the benefits of continued scaling under strict latency constraints, we introduce a two-stage architecture that offloads the heavy computation of large, long-context models to an asynchronous upstream user model. We demonstrate that upstream improvements transfer predictably to downstream ranking tasks. Deployed as the largest user model at Meta, this multi-stage framework drives a 4.3\% conversion uplift on Facebook Feed and Reels with minimal serving overhead, establishing a practical blueprint for harnessing scaling laws in industrial recommender systems.

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Lee Xiong, Zhirong Chen, Rahul Mayuranath, Shangran Qiu, Arda Ozdemir, Lu Li, Yang Hu, Dave Li, Jingtao Ren, Howard Cheng, Fabian Souto Herrera, Ahmed Agiza, Baruch Epshtein, Anuj Aggarwal, Julia Ulziisaikhan, Chao Wang, Dinesh Ramasamy, Parshva Doshi, Sri Reddy, Arnold Overwijk. 2026-01-27. LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation. https://arxiv.org/abs/2601.20083

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