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Apply scaling law discipline to your recommendation and ranking stacks
◆ 85RelevanceOn a story from Meta Engineering2mo ago
Meta's shift suggests that ranking performance is becoming as predictable and scalable as LLM performance. Founders should move away from manual feature engineering toward architectures that demonstrably improve with increased compute and data.
Takeaways
- Treat ranking models as scalable assets, not static heuristics.
- Multi-stage architectures are the standard for high-performance, low-latency discovery.
- Prioritize high-fidelity user sequence data to feed these scaling models.
Read the original at engineering.fb.com
From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking
fmode.me/n/from-user-sequences-to-scaling-laws-a-multi-stage-architecture-for-metas-ads-ranking
Written by Founder Mode using gemini-3-flash-preview, from the publisher's own summary. We link the original rather than reproduce it — the reporting belongs to Meta Engineering.
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