Large recommendation models have demonstrated substantial potential gains under scaling laws, yet these gains are difficult to realize in industrial recommendation systems because real-world deployment requires lightweight models with strict serving efficiency and latency guarantees. This creates a fundamental gap between offline model scaling and online deployment. In this work, we present Rec-Distill, an industrial distillation pipeline that transfers the performance gains of large-scale recommendation modeling to efficient serving models. Rec-Distill combines large-teacher scaling with student-side transfer optimization through decoupled training, black-box distillation, debiasing mechanism, and a hybrid batch-streaming pipeline for dynamic recommendation environments. Across multiple recommendation and advertising scenarios on real-world platforms, our framework scales teacher models up to 24B dense parameters and 20K behavior sequence length, while enabling lightweight students to recover a substantial portion of teacher gains, with distillation transferability exceeding 60% in the best setting. Extensive offline and online experiments further show that these transferred gains consistently translate into measurable business improvements under industrial constraints. These results demonstrate that Rec-Distill provides a practical framework for distilling large-scale recommendation models into deployable, cost-efficient serving systems, while also establishing a reliable path toward scaling recommendation models to even larger regimes in the future.

翻译：大规模推荐模型在缩放定律下展现出显著的性能潜力，然而这些增益在工业推荐系统中难以实现，因为实际部署要求模型具有轻量级结构、严格的推理效率与延迟保障。这导致了离线模型缩放与在线部署之间的根本性鸿沟。本文提出Rec-Distill——一种工业级蒸馏流水线，能将大规模推荐建模的性能增益转移至高效服务模型。通过解耦训练、黑盒蒸馏、去偏机制以及面向动态推荐环境的混合批流流水线，Rec-Distill实现了大规模教师模型缩放与学生端迁移优化的结合。在多个实际平台上的推荐与广告场景中，我们的框架将教师模型扩展至240亿稠密参数与20K行为序列长度，同时使轻量级学生模型能够恢复教师模型的大部分增益，最优设置下蒸馏可迁移性超过60%。大量离线与在线实验进一步表明，这些迁移的增益在工业约束条件下持续转化为可衡量的业务改进。这些结果证明，Rec-Distill提供了一种实用框架，能够将大规模推荐模型蒸馏为可部署、低成本的服务系统，同时也为未来将推荐模型扩展至更大规模建立了可靠路径。