O-Researcher: An Open Ended Deep Research Model via Multi-Agent Distillation and Agentic RL

• URL: http://arxiv.org/abs/2601.03743v1
• Date: Wed, 07 Jan 2026 09:31:10 GMT
• Title: O-Researcher: An Open Ended Deep Research Model via Multi-Agent Distillation and Agentic RL
• Authors: Yi Yao, He Zhu, Piaohong Wang, Jincheng Ren, Xinlong Yang, Qianben Chen, Xiaowan Li, Dingfeng Shi, Jiaxian Li, Qiexiang Wang, Sinuo Wang, Xinpeng Liu, Jiaqi Wu, Minghao Liu, Wangchunshu Zhou,
• Abstract summary: We introduce a novel framework for the automated synthesis of sophisticated, research-grade instructional data.<n>Our approach centers on a multi-agent workflow where collaborative AI agents simulate complex tool-integrated reasoning.<n>We develop a two-stage training strategy that integrates supervised fine-tuning with a novel reinforcement learning method.
• Score: 28.10102994309489
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The performance gap between closed-source and open-source large language models (LLMs) is largely attributed to disparities in access to high-quality training data. To bridge this gap, we introduce a novel framework for the automated synthesis of sophisticated, research-grade instructional data. Our approach centers on a multi-agent workflow where collaborative AI agents simulate complex tool-integrated reasoning to generate diverse and high-fidelity data end-to-end. Leveraging this synthesized data, we develop a two-stage training strategy that integrates supervised fine-tuning with a novel reinforcement learning method, designed to maximize model alignment and capability. Extensive experiments demonstrate that our framework empowers open-source models across multiple scales, enabling them to achieve new state-of-the-art performance on the major deep research benchmark. This work provides a scalable and effective pathway for advancing open-source LLMs without relying on proprietary data or models.

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