Step-DeepResearch Technical Report
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.
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Chen Hu
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Haikuo Du
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Heng Wang
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Lin Lin
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Mingrui Chen
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Peng Liu
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Ruihang Miao
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Tianchi Yue
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Wang You
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Wei Ji
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Wei Yuan
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Wenjin Deng
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Xiaojian Yuan
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Xiaoyun Zhang
claimed_verified
2025-12-29T14:24:55.909Z
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ZhangXiaoyun
DadaCloud01
user
DadaCloud01
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Xiangyu Liu
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Xikai Liu
claimed_verified
2025-12-29T14:24:52.962Z
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Liu Xikai
KaneAllen
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Yanming Xu
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Yicheng Cao
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Yifei Zhang
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Yongyao Wang
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Yubo Shu
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Yurong Zhang
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Yuxiang Zhang
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Zheng Gong
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Zhichao Chang
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Binyan Li
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Dan Ma
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Furong Jia
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Hongyuan Wang
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Jiayu Liu
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Jing Bai
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Junlan Liu
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Manjiao Liu
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Na Wang
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Qiuping Wu
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Qinxin Du
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Shiwei Li
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Wen Sun
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Yifeng Gong
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Yonglin Chen
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Yuling Zhao
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Yuxuan Lin
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Ziqi Ren
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Zixuan Wang
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Aihu Zhang
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Brian Li
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Buyun Ma
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Kang An
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Li Xie
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Mingliang Li
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Pan Li
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Shidong Yang
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Xi Chen
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Xiaojia Liu
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Yuchu Luo
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Yuan Song
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YuanHao Ding
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Yuanwei Liang
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Zexi Li
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Zhaoning Zhang
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Zixin Zhang
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Binxing Jiao
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Daxin Jiang
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Jiansheng Chen
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Jing Li
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Xiangyu Zhang
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Yibo Zhu
Step-DeepResearch Technical Report
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.
Step-DeepResearch, an end-to-end agent enhanced with a data synthesis strategy and progressive training, achieves expert-level capabilities in deep research scenarios, outperforming established models.
Deep Research BrowseComp Step-DeepResearch Data Synthesis Strategy Atomic Capabilities agentic mid-training SFT RL Checklist-style Judger ADR-Bench Scale AI Research Rubrics OpenAI Gemini DeepResearch