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Academic Team Ends Tech Giants' Monopoly with SFT; OpenSeeker-v2 Tops Search Agent Rankings

Academic Team Ends Tech Giants' Monopoly with SFT; OpenSeeker-v2 Tops Search Agent Rankings

July 4, 2026
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In the evolving landscape of large language models (LLMs), deep search capabilities have become the decisive advantage for advanced intelligent agents. Yet this arena has long been controlled by well-funded industry giants. Conventional development approaches typically depend on resource-heavy pipelines that encompass pre-training, continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning (RL).

A team of academic researchers has recently unveiled their latest work, OpenSeeker-v2, fundamentally challenging this established view. According to their report, training on high-quality, high-difficulty task trajectories enables even a straightforward supervised fine-tuning (SFT) approach to produce a top-tier search agent.

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The team outlined three key optimization strategies for data synthesis: first, scaling up the knowledge graph to create a broader exploration space; second, substantially expanding the toolkit repertoire to push functional limits; and finally, applying strict low-step filtering to guarantee the quality and efficiency of training data.

Experimental results reveal that OpenSeeker-v2 (with 30B parameters and a ReAct architecture), trained on merely 10,600 data points, exhibited commanding performance across four core benchmarks: it scored 46.0% on BrowseComp, 58.1% on BrowseComp-ZH, 34.6% on "Humanity's Last Exam", and 78.0% on xbench. These figures not only set new records but also comprehensively outperformed industry models that relied on heavy pipelines combining CPT, SFT, and RL—such as Tongyi DeepResearch.

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Strikingly, this marks the first state-of-the-art (SOTA) search agent developed by an entirely academic team using only SFT, at the same model scale and architecture. The team has now open-sourced the model weights for OpenSeeker-v2. This breakthrough significantly lowers the R&D barrier for advanced search agents and offers a more accessible, lightweight development pathway for both the academic and open-source communities.

Paper: https://arxiv.org/pdf/2605.04036

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