Fetched August 10th, 2026
Meta
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From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking
How to efficiently rank ads at scale by leveraging temporal signals from user action sequences rather than relying on static, manually engineered sparse features across billions of daily interactions.
ml-systems
real-time-systems
Meta
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GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model
Meta needed to double the training efficiency of GEM, its LLM-scale ads foundation model, while scaling training compute 4x across thousands of GPUs without proportional increases in training time and cost.
distributed-systems
ml-systems
Fetched August 3rd, 2026
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