[2603.24226] UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking

Computer Science > Information Retrieval arXiv:2603.24226 (cs) [Submitted on 25 Mar 2026] Title:UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking Authors:Liren Yu, Caiyuan Li, Feiyi Dong, Tao Zhang, Zhixuan Zhang, Dan Ou, Haihong Tang, Bo Zheng View a PDF of the paper titled UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking, by Liren Yu and 7 other authors View PDF HTML (experimental) Abstract:Recent advances in Large Language Models (LLMs) have inspired a surge of scaling law research in industrial search, advertising, and recommendation systems. However, existing approaches focus mainly on architectural improvements, overlooking the critical synergy between data and architecture design. We observe that scaling model parameters alone exhibits diminishing returns, i.e., the marginal gain in performance steadily declines as model size increases, and that the performance degradation caused by complex heterogeneous data distributions is often irrecoverable through model design alone. In this paper, we propose UniScale to address these limitation, a novel co-design framework that jointly optimizes data and architecture to unlock the full potential of model scaling, which includes two core parts: (1) ES$^3$ (Entire-Space Sample System), a high-quality data scaling system that expands the training signal beyond conventional sampling strategies from both intra-domain request contexts with global supervised signal constructed b...