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.