LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation

Shali Jiang; Hua Zheng; Boyang Liu; Laming Chen; Kenny Lov; Chuanqi Xu; Lisang Ding; Qinghai Zhou; Can Cui; Xiaolong Liu; Xiaoyi Liu; Yasmine Badr; Xin Xu; Jiyan Yang; Ellie Dingqiao Wen; Gerard Jonathan Mugisha Akkerhuis; Chenxiao Guan; Rong Jin; Ruichao Qiu; Xian Chen; Shifu Xu; Zhehui Zhou; Ping Chen; Rui Yang; Haicheng Chen; Xiangge Meng; Song Zhou; Dharak Kharod; Shuyu Xu; Qiang Jin; Qiao Yang; Wankun Zhu; Qin Huang; Yuzhen Huang; Darren Liu; Parish Aggarwal; Hui Zhou; Erzhuo Wang; Shuo Chang; Xiaorui Gan; Wenlin Chen; Santanu Kolay; Huayu Li

arXiv · 2605.29280

LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation

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Abstract

Knowledge distillation (KD) transfers a single scalar prediction from a large foundation model (FM) to compact vertical models (VMs), suffering from diminishing transfer ratio -- the fraction of FM improvement captured by the VM -- as a single scalar cannot convey the rich intermediate knowledge that larger FMs learn. To address this bottleneck, we propose LoopFM (Learning frOm HistOrical RePresentations of FM), a framework that opens a high-bandwidth transfer channel by structuring FM intermediate embeddings as input features (e.g., user history sequence) for downstream VMs, without requiring real-time FM inference at serving and architectural coupling between FM and VM. We provide a theoretical framework for LoopFM with a gain decomposition and transfer-ratio analysis. On three public benchmarks, LoopFM demonstrates strong AUC improvements (e.g., 6%+ on TaobaoAd) and complementary knowledge transfer capability with KD. On industrial-scale systems (billions of examples, trillion-parameter FMs), LoopFM approximately doubles the knowledge transfer ratio on top of KD, delivering a +0.5% conversion improvement in the first half after its initial launch, and +1.03% and +1.22% conversion improvement from two individual launches in the subsequent half.

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Shali Jiang, Hua Zheng, Boyang Liu, Laming Chen, Kenny Lov, Chuanqi Xu, Lisang Ding, Qinghai Zhou, Can Cui, Xiaolong Liu, Xiaoyi Liu, Yasmine Badr, Xin Xu, Jiyan Yang, Ellie Dingqiao Wen, Gerard Jonathan Mugisha Akkerhuis, Chenxiao Guan, Rong Jin, Ruichao Qiu, Xian Chen, Shifu Xu, Zhehui Zhou, Ping Chen, Rui Yang, Haicheng Chen, Xiangge Meng, Song Zhou, Dharak Kharod, Shuyu Xu, Qiang Jin, Qiao Yang, Wankun Zhu, Qin Huang, Yuzhen Huang, Darren Liu, Parish Aggarwal, Hui Zhou, Erzhuo Wang, Shuo Chang, Xiaorui Gan, Wenlin Chen, Santanu Kolay, Huayu Li. 2026-06-02. LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation. https://arxiv.org/abs/2605.29280

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