The rapidly evolving landscape of products, surfaces, policies, and regulations poses significant challenges for deploying state-of-the-art recommendation models at industry scale, primarily due to data fragmentation across domains and escalating infrastructure costs that hinder sustained quality improvements.

Recent advances in recommendation scaling laws have led to foundation models of unprecedented complexity. While these models offer superior performance, their computational demands make real-time serving impractical, often forcing practitioners to rely on knowledge distillation—compromising serving quality for efficiency. To address this challenge, we present SOLARIS (Speculative Offloading of Latent-bAsed Representation for Inference Scaling), a novel framework inspired by speculative decoding. SOLARIS proactively precomputes user-item interaction embeddings by predicting which user-item pairs are likely to appear in future requests, and asynchronously generating their foundation model representations ahead of time. This approach decouples the costly foundation model inference from the latency-critical serving path, enabling real-time knowledge transfer from models previously considered too expensive for online use. Deployed across Meta's advertising system serving billions of daily requests, SOLARIS achieves 0.67% revenue-driving top-line metrics gain, demonstrating its effectiveness at scale.

The volume of ads ranked impacts the performance of ad ranking systems.To enhance efficiency, multi-stage ranking systems are widely studied in academia and adopted across industry.However, as large-scale deep learning recommendation models gain prevalence, resource constraints-especially CPU and GPU limitationshave become a significant bottleneck.These constraints can hinder model iteration and lead to incomplete ranking, causing regressions in user experience and ad performance.To address these issues, we analyzed ad ranking metrics and found that ad rankings for individual users remain relatively stable over short periods.Based on this insight, we introduce Negative Exclusion Filtering, a framework that optimizes the balance between ranked ad volume and computing resources.By skipping re-ranking of consistently low-ranked ads for each user request, it reduces computing cost in large-scale social media environments.