An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision

The whole-page reranking plays a critical role in shaping the user experience of search engines, which integrates retrieval results from multiple modalities, such as documents, images, videos, and LLM outputs. Existing methods mainly rely on large-scale human-annotated data, which is costly to obtain and time-consuming. This is because whole-page annotation is far more complex than single-modal: it requires assessing the entire result page while accounting for cross-modal relevance differences. Thus, how to improve whole-page reranking performance while reducing annotation costs is still a key challenge in optimizing search engine result pages(SERP). In this paper, we propose SMAR, a novel whole-page reranking framework that leverages strong Single-modal rankers to guide Modal-wise relevance Alignment for effective Reranking, using only limited whole-page annotation to outperform fully-annotated reranking models. Specifically, high-quality single-modal rankers are first trained on data specific to their respective modalities. Then, for each query, we select a subset of their outputs to construct candidate pages and perform human annotation at the page level. Finally, we train the whole-page reranker using these limited annotations and enforcing consistency with single-modal preferences to maintain ranking quality within each modality. Experiments on the Qilin and Baidu datasets demonstrate that SMAR reduces annotation costs by about 70-90% while achieving significant ranking improvements compared to baselines. Further offline and online A/B testing on Baidu APPs also shows notable gains in standard ranking metrics as well as user experience indicators, fully validating the effectiveness and practical value of our approach in real-world search scenarios.