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

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• Sequence modeling in recommendation systems follows predictable power-law scaling similar to large language models (LLMs).result 90%
• Semantic features are a prerequisite for scaling in recommendation sequence models, enabling effective utilization of deeper and longer architectures.technical 85%
• A two-stage architecture that offloads heavy computation to an asynchronous upstream user model enables continued scaling under strict latency constraints.method 88%
• Upstream model improvements transfer predictably to downstream ranking tasks.result 82%
• The LLaTTE multi-stage framework achieved a 4.3% conversion uplift on Facebook Feed and Reels with minimal serving overhead.result 92%
• The approach establishes a practical blueprint for harnessing scaling laws in industrial recommender systems.market 80%
• The method relies heavily on precise data handling and model tuning, which could be challenging in different production environments.limitation 75%
• The method might struggle with real-time data changes due to its reliance on precomputed embeddings.limitation 70%

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