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Lightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems

arXiv · AI, language, vision and robotics · article · Sep 21, 2026 · UTC

Modern production-scale recommender systems rely on complex, multi-task ranking models. Introducing new prediction tasks into these massive systems often causes bottlenecks - it risks negative task conflicts with existing tasks, and can lead to long development and experimentation cycles due to the expensive retraining of backbone models and downstream models or tuning of reward combination formulas. To address the critical challenge of slow experimentation velocity, we introduce the Lightweight Ranking Heads (Light Heads) framework. Designed for continuous online learning environments, Light

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