From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking
Source
engineering.fb.com
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Why it matters
Splitting expensive offline user-sequence modeling from lightweight online ranking is what let Meta scale sequence learning into a platform with predictable, LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition →-style scaling laws, with dense tokenization and target-aware attentionThe mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.Full definition → replacing hand-engineered interactions.
Terms in this piece · Glossary
attention — The mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
