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arXiv · 2604.19550

LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction

Jiakai Tang·Runfeng Zhang·Weiqiu Wang·Yifei Liu·Chuan Wang·Xu Chen·Yeqiu Yang·Jian Wu·Yuning Jiang·Bo Zheng
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Abstract

Scaling Transformer-based click-through rate (CTR) models by stacking more parameters brings growing computational and storage overhead, creating a widening gap between scaling ambitions and the stringent industrial deployment constraints. We propose LoopCTR, which introduces a loop scaling paradigm that increases training-time computation through recursive reuse of shared model layers, decoupling computation from parameter growth. LoopCTR adopts a sandwich architecture enhanced with Hyper-Connected Residuals and Mixture-of-Experts, and employs process supervision at every loop depth to encode multi-loop benefits into the shared parameters. This enables a train-multi-loop, infer-zero-loop strategy where a single forward pass without any loop already outperforms all baselines. Experiments on three public benchmarks and one industrial dataset demonstrate state-of-the-art performance. Oracle analysis further reveals 0.02--0.04 AUC of untapped headroom, with models trained with fewer loops exhibiting higher oracle ceilings, pointing to a promising frontier for adaptive inference.

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Jiakai Tang, Runfeng Zhang, Weiqiu Wang, Yifei Liu, Chuan Wang, Xu Chen, Yeqiu Yang, Jian Wu, Yuning Jiang, Bo Zheng. 2026-04-21. LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction. https://arxiv.org/abs/2604.19550

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