UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking
Recent advances in Large Language Models (LLMs) have inspired a surge of scaling 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, and that the performance degradation caused by complex heterogeneous data distributions is often irrecoverable through model design alone. In this paper, we propose UniScale, a novel co-design framework that jointly optimizes data and architecture to unlock the full potential of model scaling. UniScale includes two core p...
| Authors | Liren Yu, Caiyuan Li, Feiyi Dong et al. |
|---|---|
| Primary category | cs.IR |
| Published | 25 March 2026 |
| Last updated at source | 11 August 2026 |
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