Step-DeepResearch Technical Report
Authors: name: Chen Hu; name: Haikuo Du; name: Heng Wang; name: Lin Lin; name: Mingrui Chen; name: Peng Liu; name: Ruihang Miao; name: Tianchi Yue; name: Wang You; name: Wei Ji; name: Wei Yuan; name: Wenjin Deng; name: Xiaojian Yuan; name: Xiaoyun Zhang; name: Xiangyu Liu; name: Xikai Liu; name: Yanming Xu; name: Yicheng Cao; name: Yifei Zhang; name: Yongyao Wang; name: Yubo Shu; name: Yurong Zhang; name: Yuxiang Zhang; name: Zheng Gong; name: Zhichao Chang; name: Binyan Li; name: Dan Ma; name: Furong Jia; name: Hongyuan Wang; name: Jiayu Liu; name: Jing Bai; name: Junlan Liu; name: Manjiao Liu; name: Na Wang; name: Qiuping Wu; name: Qinxin Du; name: Shiwei Li; name: Wen Sun; name: Yifeng Gong; name: Yonglin Chen; name: Yuling Zhao; name: Yuxuan Lin; name: Ziqi Ren; name: Zixuan Wang; name: Aihu Zhang; name: Brian Li; name: Buyun Ma; name: Kang An; name: Li Xie; name: Mingliang Li; name: Pan Li; name: Shidong Yang; name: Xi Chen; name: Xiaojia Liu; name: Yuchu Luo; name: Yuan Song; name: YuanHao Ding; name: Yuanwei Liang; name: Zexi Li; name: Zhaoning Zhang; name: Zixin Zhang; name: Binxing Jiao; name: Daxin Jiang; name: Jiansheng Chen; name: Jing Li; name: Xiangyu Zhang; name: Yibo Zhu
Published: 2025-12-23 16:32:27

Abstract: As LLMs shift toward autonomous agents, Deep Research has emerged as a pivotal metric. However, existing academic benchmarks like BrowseComp often fail to meet real-world demands for open-ended research, which requires robust skills in intent recognition, long-horizon decision-making, and cross-source verification. To address this, we introduce Step-DeepResearch, a cost-effective, end-to-end agent. We propose a Data Synthesis Strategy Based on Atomic Capabilities to reinforce planning and report writing, combined with a progressive training path from agentic mid-training to SFT and RL. Enhanced by a Checklist-style Judger, this approach significantly improves robustness. Furthermore, to bridge the evaluation gap in the Chinese domain, we establish ADR-Bench for realistic deep research scenarios. Experimental results show that Step-DeepResearch (32B) scores 61.4% on Scale AI Research Rubrics. On ADR-Bench, it significantly outperforms comparable models and rivals SOTA closed-source models like OpenAI and Gemini DeepResearch. These findings prove that refined training enables medium-sized models to achieve expert-level capabilities at industry-leading cost-efficiency.