发表机构
Alibaba Group（阿里巴巴集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电商检索中双编码器与重排器的目标不匹配问题，提出PAO选择性RL优化方法，在冻结文档索引下提升检索性能且优于标准RL与蒸馏基线。
AI 中文摘要
在大规模电商检索场景中，双编码器检索器针对对比相似度进行优化，而下游重排器则捕捉更细粒度的相关性偏好；这种目标不匹配限制了端到端检索质量。强化学习提供了利用奖励模型反馈适配检索器的途径，但我们发现标准策略梯度更新会损害嵌入几何结构，尤其是在工业场景下文档索引必须保持冻结时。为解决该问题，我们提出PAO（仅正优势）这一选择性强化学习优化方法。我们的分析表明，在冻结的高维空间中不加区分地惩罚负样本（推离）会破坏预训练的语义流形。PAO仅对具有正优势的检索项应用梯度更新，有效将查询嵌入拉向高奖励区域，同时保留全局拓扑稳定性。在大规模工业数据集和公开基准上的实验表明，PAO显著优于标准强化学习和蒸馏基线方法。
英文摘要
In large-scale e-commerce retrieval, dual-encoder retrievers are op- timized for contrastive similarity, whereas downstream rerankers capture finer-grained relevance preferences; this objective mis- match limits end-to-end retrieval quality. Reinforcement Learning offers a way to use reward-model feedback for retriever adaptation, but we observe that standard policy-gradient updates can degrade embedding geometry, especially when the document index must remain frozen due to industrial constraints. To address this, we propose PAO (Positive-Advantage-Only), a selective RL optimization method. Our analysis reveals that in- discriminate penalization of negative samples (pushing away) in a frozen high-dimensional space disrupts pre-trained semantic man- ifolds. PAO selectively applies gradient updates only to retrieved items with positive advantages, effectively pulling query embed- dings toward high-reward regions while preserving global topo- logical stability. Experiments on both a massive industrial dataset and public benchmarks demonstrate that PAO significantly outper- forms standard RL and distillation baselines.
