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