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
Organization: StepFun

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.