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 limitations, 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 by hierarchical label attribution and cross-domain samples aligning with the essence of user decision under similar content exposure environment in search domain; and (2) HHSFT (Heterogeneous Hierarchical Sample Fusion Transformer), a novel architecture designed to effectively model the complex heterogeneous distribution of scaled data and to harness the entire space user behavior data with Heterogeneous Hierarchical Feature Interaction and Entire Space User Interest Fusion, thereby surpassing the performance ceiling of structure-only model tuning. Extensive experiments demonstrate that UniScale achieves significant improvements through the synergistic co-design of data and architecture and exhibits scaling trends. Online A/B tests on a real-world e-commerce search platform further show gains of 1.70% in purchase and 2.04% in Gross Merchandise Volume (GMV).

翻译：近期大型语言模型的进展激发了工业搜索、广告及推荐系统中扩展律研究的热潮。然而现有方法主要聚焦于架构改进，忽视了数据与架构设计之间的关键协同效应。我们观察到仅扩展模型参数会出现收益递减现象，即随着模型规模增大，性能的边际增益持续下降，且复杂异构数据分布导致的性能退化往往无法仅通过模型设计来恢复。本文提出UniScale以解决上述局限，这是一种联合优化数据与架构的新型协同设计框架，旨在充分释放模型扩展潜力。该框架包含两个核心部分：(1) ES³（全空间样本系统），一种高质量数据扩展系统，通过层级化标签归因构建的全局监督信号从域内请求上下文扩展训练信号，并结合跨域样本（这些样本与搜索域中相似内容曝光环境下用户决策的本质保持一致），突破传统采样策略；(2) HHSFT（异构分层样本融合Transformer），一种新型架构，通过异构分层特征交互与全空间用户兴趣融合，有效建模扩展数据的复杂异构分布并充分利用全空间用户行为数据，从而超越仅调整模型结构的性能上限。大量实验表明，UniScale通过数据与架构的协同设计实现了显著性能提升，并展现出扩展趋势。在真实电商搜索平台上的在线A/B测试进一步显示，购买量提升1.70%，商品交易总额（GMV）提升2.04%。