Dense retrieval is a fundamental component of e-commerce search, projecting user queries and items into a shared semantic representation space for large-scale, real-time retrieval. While LLM-based embedding models are increasingly replacing BERT-style encoders, prevalent training pipelines still largely rely on BERT-like supervised fine-tuning and hard negative mining. Such paradigms require intricate offline negative construction, thereby constraining model iteration efficiency and limiting the evolution of learned semantic representations. We propose Retrieval-GRPO, a dense retrieval framework based on reinforcement learning. It dispenses with offline hard negative mining by performing dynamic Top-K candidates retrieval during training. Moreover, we introduce a relevance-oriented LLM as a reward model to provide real-time supervisory signals. Guided by the resulting reinforcement learning objective, the retriever updates embeddings to better capture semantic correspondence and achieve improved generalization on complex long-tail queries. Extensive offline and online experiments substantiate the effectiveness of Retrieval-GRPO in Taobao Search, which serves China’s largest e-commerce platform.

Generative Retrieval (GR) formulates retrieval as a sequence-to-sequence generation task, assigning each document a document identifier (DocID) and retrieving it through autoregressive decoding, making DocID design a critical factor in retrieval quality. However, existing schemes based on discrete representation learning suffer from inherent collision issues and create a mismatch between the DocID’s encoding objective and the system’s business optimization target. To address these limitations, we propose Cluster-Ranked Identifier (CRID), which decouples DocID into semantic clustering and business-value ranking, yielding collision-free identifiers that support incremental updates via intra-cluster reranking. We further introduce an analytical framework that decomposes retrieval gains into personalized preference and statistical prior generalization, revealing how semantic cluster size governs the balance between the two components. Experiments on a Taobao e-commerce corpus of over 300M items show that CRID surpasses the strongest embedding-based retrieval baseline on top-K Hitrate, and delivers +1.06% GMV in full-traffic deployment.