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OpenResearcher pipeline enables offline synthesis of AI research trajectories

Researchers have developed OpenResearcher, an open-source pipeline designed for synthesizing long-horizon research trajectories for training deep research agents. This pipeline operates offline, utilizing three explicit browser primitives over a 15 million document corpus, which allows for reproducible and cost-effective data collection compared to proprietary APIs. By using GPT-OSS-120B as a teacher model, they generated over 97,000 trajectories, leading to a significant improvement in accuracy on benchmarks like BrowseComp-Plus when a 30B-A3B model was fine-tuned on this data. AI

IMPACT Provides a reproducible and cost-effective method for generating training data for deep research agents, potentially accelerating development in this area.

RANK_REASON The cluster contains an academic paper detailing a new pipeline and methodology for AI research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

OpenResearcher pipeline enables offline synthesis of AI research trajectories

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The cluster contains an academic paper detailing a new pipeline and methodology for AI research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zhuofeng Li, Dongfu Jiang, Xueguang Ma, Haoxiang Zhang, Ping Nie, Yuyu Zhang, Kai Zou, Jianwen Xie, Yu Zhang, Wenhu Chen ·

    OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis

    arXiv:2603.20278v2 Announce Type: replace-cross Abstract: Training deep research agents requires long-horizon trajectories that interleave search, evidence aggregation, and multi-step reasoning. However, existing data collection pipelines typically rely on proprietary web APIs, m…