轻量级排序头：加速生产推荐系统中的多任务实验
Lightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems
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中文总结 AI 辅助
针对生产推荐系统多任务实验迭代慢的问题，提出轻量级排序头框架，通过动态注入新任务、停止梯度和无状态训练，避免重训练，将迭代周期从数周缩短至数天。
中文摘要 AI 辅助
现代生产级推荐系统依赖于复杂的多任务排序模型。将新的预测任务引入这些大规模系统常常导致瓶颈——它可能带来与现有任务的负面任务冲突，并且由于骨干模型和下游模型的昂贵重训练或奖励组合公式的调整，可能导致开发和实验周期变长。为了解决实验速度慢这一关键挑战，我们引入了轻量级排序头（Light Heads）框架。专为连续在线学习环境设计，Light Heads 能够将新任务动态注入现有的多任务排序模型，有效避免了模型冷启动和骨干模型重训练的需求。通过利用停止梯度和无状态每日训练，该设计严格隔离新任务，降低了不利任务冲突的风险。至关重要的是，该框架使用集中式配置，允许将 Light Heads 同时添加到多个模型中，从而加速训练数据生成和下游模型的协同训练。该方案已在 YouTube 规模成功部署，将多任务实验的迭代周期从数周缩短至数天。在本文中，我们详细介绍了系统架构，分析了无状态冷启动头的训练动态，将其性能与完整头进行了比较，并展示了 Light Heads 如何实现快速 A/B 实验和部署新的排序任务，从而产生可衡量的生产价值。
英文摘要
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 Heads enable the dynamic injection of new tasks into existing multi-task ranking models, effectively obviating the need for model cold-starting and retraining of backbone models. By utilizing stop-gradients and stateless daily training, this design strictly isolates new tasks, mitigating the risk of adverse task conflicts. Crucially, this framework uses a centralized configuration that allows Light Heads to be added to multiple models simultaneously, unblocking faster training data generation and co-training of downstream models. Successfully deployed at YouTube scale, this approach reduces the iteration cycle for multi-task experimentation from several weeks to days. In this paper, we detail the system architecture, analyze the training dynamics of stateless cold-started heads, compare their performance to full heads, and demonstrate how Light Heads have enabled the rapid A/B experimentation and deployment of new ranking tasks that yield measurable production value.
发表机构
- Google LLC（谷歌有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。
