Towards Efficient Whole Page Ranking with One-Pass Inference

Authors: Qi Xu , Zhigang Hua , Zijie Lei , Yan Xie , Jizhe Zhang , Lynn Lu , Delun Du , Sha Hua , Ren Mao , Ke Pan , Haomin Yu , Sandeep Pandey , Luke Simon

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Abstract: Whole-page ranking jointly optimizes advertisements and organic content to balance business outcomes and user engagement under complex layout and business constraints. Existing non-autoregressive (NAR) approaches enable efficient page generation but typically score candidate items independently of previously selected items, limiting their ability to capture order-dependent interactions. Autoregressive (AR) methods address this limitation through sequential conditioning, but incur substantial inference overhead that challenges large-scale deployment. Bridging this quality-efficiency gap remains a challenge in industrial whole-page ranking.

We propose Multi-Item Scoring (MIS), a prefix-aware NAR-style framework that incorporates sequential context into efficient multi-item scoring. MIS integrates global candidate interactions, localized prefix conditioning, and position-aware scoring within a shared representation architecture. By computing reusable representations once per request and performing lightweight prefix-conditioned scoring during constrained beam search, MIS captures important sequential dependencies without repeated full-model inference.

Experiments on KuaiRand-1K and RL4RS demonstrate that MIS approaches the generation quality of an autoregressive CGA baseline while achieving 4.07x and 3.54x faster CPU decoding, respectively. In a randomized evaluation on a large-scale social-video platform, MIS improves the realization metric by 0.104% and watch time by 0.058%, while reducing ranking-service CPU consumption by 9.73%. These results demonstrate that prefix-aware multi-item scoring can narrow the quality gap between NAR and AR generation while retaining the efficiency required for industrial scalability.

Comments:

12 pages, 2 figures, 5 tables

Subjects:

Artificial Intelligence (cs.AI) ; Machine Learning (stat.ML)

Cite as:

arXiv:2611.05542 [cs.AI]

(or

arXiv:2611.05542v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.05542

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Qi Xu [ view email ]

[v1]

Tue, 3 Nov 2026 19:33:22 UTC (625 KB)

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