Last year, we introduced GEM , the industry’s largest recommendation system (RecSys) foundation model. GEM established Meta’s first LLM-inspired paradigm for ads recommendation, driving major performance gains.
What happened
Building on this foundation, our next goal is to further scale both compute and intelligence transfer with next-generation hardware, and expand this capability across Meta’s entire ads ecosystem. This spans dozens of surfaces and objectives, delivering seamless full-funnel optimization across awareness, consideration, and conversions.
Where the sources line up
The 2026 generation of GEM shifts from isolated per-domain optimizations — such as Facebook, Instagram, Reels — to a single central foundation model. It learns from data and signals across all of Meta’s ads platforms, bends the scaling curve at LLM-scale computation, and delivers strong advertiser outcomes through post-training techniques that provide a larger latency budget without requiring real-time evaluation of all ad candidates — all on a substantially more efficient training stack. The result is simultaneous ad performance gains across every surface and funnel stage, and deeper understanding that better matches ads to what people are looking for.
Practical impact for readers
GEM has delivered 18. 9% conversion improvement and 12% ad click-through rate (CTR) improvement across Meta’s ad surfaces and objectives, and additionally contributed 5. 8%, 4. 1%, and 1. 6% conversion uplift on Facebook, Instagram, and off-site conversion for targeted users over the past year. These gains span both upper-funnel surfaces and deep-funnel optimization goals, driving stronger returns for advertisers.
Who should pay attention now
Challenge: Empirical scaling laws demonstrate that model performance predictably improves with compute, parameter, and data size using transformer model architecture. However, recommender system models do not exhibit similar scaling properties with transformer due to massive heterogeneous features and shifting distributions.
What is still unclear
Solution: We built an LLM-style architecture — Kunlun , co-designed with the latest GPU hardware, to make larger, deeper models substantially more cost-efficient and to enable long-context learning over user engagement history. We observed an LLM-like scaling law at our largest pre-training scale with 2. 5x model complexity growth from earlier versions of GEM. The pattern mirrors frontier LLMs: more capacity and longer context produce emergent gains in ranking quality that translate directly into business results.
Latest comments
0No comments yet. You can start the conversation.