OpenSeeker-v2：通过信息丰富的高难度轨迹突破搜索智能体的性能极限

OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories

May 5, 2026

作者: Yuwen Du, Rui Ye, Shuo Tang, Keduan Huang, Xinyu Zhu, Yuzhu Cai, Siheng Chen

cs.AI

摘要

深度搜索能力已成为前沿大语言模型智能体的必备核心能力，但其开发仍由行业巨头主导。典型的工业级方案需要经历预训练、持续预训练、监督微调和强化学习这一资源密集型流程。本报告表明，当注入信息丰富且高难度的决策轨迹时，简单的监督微调方法在训练前沿搜索智能体时展现出惊人潜力。通过三项数据合成改进：扩展知识图谱规模以丰富探索路径、增加工具集规模以拓宽功能范围、实施严格低步数过滤，我们建立了更强的基线模型。仅使用1.06万条数据训练的OpenSeeker-v2，在四大基准测试中（采用ReAct范式的30B规模智能体）实现领先性能：BrowseComp达46.0%、BrowseComp-ZH达58.1%、人类终极考试达34.6%、xbench达78.0%，全面超越采用繁重CPT+SFT+RL流程训练的通义深度研究（相应成绩为43.4%、46.7%、32.9%和75.0%）。值得关注的是，OpenSeeker-v2是首个在其模型规模与范式下、由纯学术团队仅通过监督微调实现顶尖水平的搜索智能体。我们激动地开源OpenSeeker-v2模型权重，分享这一简洁而有效的发现，助力前沿搜索智能体研究走向更开放的研究社区。

English

Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by industrial giants. The typical industry recipe involves a highly resource-intensive pipeline spanning pre-training, continual pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL). In this report, we show that when fueled with informative and high-difficulty trajectories, a simple SFT approach could be surprisingly powerful for training frontier search agents. By introducing three simple data synthesis modifications: scaling knowledge graph size for richer exploration, expanding the tool set size for broader functionality, and strict low-step filtering, we establish a stronger baseline. Trained on merely 10.6k data points, our OpenSeeker-v2 achieves state-of-the-art performance across 4 benchmarks (30B-sized agents with ReAct paradigm): 46.0% on BrowseComp, 58.1% on BrowseComp-ZH, 34.6% on Humanity's Last Exam, and 78.0% on xbench, surpassing even Tongyi DeepResearch trained with heavy CPT+SFT+RL pipeline, which achieves 43.4%, 46.7%, 32.9%, and 75.0%, respectively. Notably, OpenSeeker-v2 represents the first state-of-the-art search agent within its model scale and paradigm to be developed by a purely academic team using only SFT. We are excited to open-source the OpenSeeker-v2 model weights and share our simple yet effective findings to make frontier search agent research more accessible to the community.