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

• URL: http://arxiv.org/abs/2509.10446v1
• Date: Fri, 12 Sep 2025 17:52:35 GMT
• Title: DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL
• Authors: Rui Lu, Zhenyu Hou, Zihan Wang, Hanchen Zhang, Xiao Liu, Yujiang Li, Shi Feng, Jie Tang, Yuxiao Dong,
• Abstract summary: We present DeepDive to advance deep search agents.<n>We propose a strategy to automatically synthesize complex, difficult, and hard-to-find questions from open knowledge graphs.<n>Experiments show that DeepDive-32B achieves a new open-source competitive result on BrowseComp.
• Score: 60.47878242100153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Augmenting large language models (LLMs) with browsing tools substantially improves their potential as deep search agents to solve complex, real-world tasks. Yet, open LLMs still perform poorly in such settings due to limited long-horizon reasoning capacity with browsing tools and the lack of sufficiently difficult supervised data. To address these challenges, we present DeepDive to advance deep search agents. First, we propose a strategy to automatically synthesize complex, difficult, and hard-to-find questions from open knowledge graphs. Second, we apply end-to-end multi-turn reinforcement learning (RL) to enhance LLMs' long-horizon reasoning with deep search. Experiments show that DeepDive-32B achieves a new open-source competitive result on BrowseComp, outperforming WebSailor, DeepSeek-R1-Browse, and Search-o1. We demonstrate that multi-turn RL training improves deep search ability and significantly contributes to the performance improvements across multiple benchmarks. We observe that DeepDive enables test-time scaling of tool calls and parallel sampling. All datasets, models, and code are publicly available at https://github.com/THUDM/DeepDive.

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