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

arXiv: https://arxiv.org/abs/2604.19550 · HTML: https://mlsanity.com/paper/2604.19550/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2026.

## Built on by (most cited first, 2 of 2)

- [Beyond Interaction Capacity: Estimator Scaling with Recursive Models for CTR Prediction](https://mlsanity.com/paper/2609.37905/) (2026)
- [Chain Dimension Scaling: Communication-Efficient Loop-Style Scaling for Industrial Short-Video Ranking](https://mlsanity.com/paper/2610.16415/) (2026)

## Abstract (arXiv)

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.