DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL

In this episode, we discuss DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL by Rui Lu, Zhenyu Hou, Zihan Wang, Hanchen Zhang, Xiao Liu, Yujiang Li, Shi Feng, Jie Tang, Yuxiao Dong. The paper introduces DeepDive, a method to improve large language models' deep search capabilities by automatically generating complex questions and applying multi-turn reinforcement learning for enhanced long-horizon reasoning. DeepDive-32B outperforms existing open-source models on browsing benchmarks like BrowseComp. The approach also enables scalable tool usage during inference, with all resources made publicly available.