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.

Query-focused meeting summarization(QFMS) aims to generate a specific summary for the given query according to the meeting transcripts. Due to the conflict between long meetings and limited input size, previous works mainly adopt extract-then-summarize methods, which use extractors to simulate binary labels or ROUGE scores to extract utterances related to the query and then generate a summary. However, the previous approach fails to fully use the comparison between utterances. To the extractor, comparison orders are more important than specific scores. In this paper, we propose a Ranker-Generator framework. It learns to rank the utterances by comparing them in pairs and learning from the global orders, then uses top utterances as the generator’s input. We show that learning to rank utterances helps to select utterances related to the query effectively, and the summarizer can benefit from it. Experimental results on QMSum show that the proposed model outperforms all existing multi-stage models with fewer parameters.

Supervised open relation extraction aims to discover novel relations by leveraging supervised data of pre-defined relations. However, most existing methods do not achieve effective knowledge transfer from pre-defined relations to novel relations, they have difficulties generating high-quality pseudo-labels for unsupervised data of novel relations and usually suffer from the error propagation issue. In this paper, we propose a Cluster-aware Pseudo-Labeling (CaPL) method to improve the pseudo-labels quality and transfer more knowledge for discovering novel relations. Specifically, the model is firstly pre-trained with the pre-defined relations to learn the relation representations. To improve the pseudo-labels quality, the distances between each instance and all cluster centers are used to generate the cluster-aware soft pseudo-labels for novel relations. To mitigate the catastrophic forgetting issue, we design the consistency regularization loss to make better use of the pseudo-labels and jointly train the model with both unsupervised and supervised data. Experimental results on two public datasets demonstrate that our proposed method achieves new state-of-the-arts performance.