Tongyi DeepResearch Technical Report
We present Tongyi DeepResearch, an agentic large language model, which is
specifically designed for long-horizon, deep information-seeking research
tasks. To incentivize autonomous deep research agency, Tongyi DeepResearch is
developed through an end-to-end training framework that combines agentic
mid-training and agentic post-training, enabling scalable reasoning and
information seeking across complex tasks. We design a highly scalable data
synthesis pipeline that is fully automatic, without relying on costly human
annotation, and empowers all training stages. By constructing customized
environments for each stage, our system enables stable and consistent
interactions throughout. Tongyi DeepResearch, featuring 30.5 billion total
parameters, with only 3.3 billion activated per token, achieves
state-of-the-art performance across a range of agentic deep research
benchmarks, including Humanity's Last Exam, BrowseComp, BrowseComp-ZH,
WebWalkerQA, xbench-DeepSearch, FRAMES and xbench-DeepSearch-2510. We
open-source the model, framework, and complete solutions to empower the
community.
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Tongyi DeepResearch Team
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Baixuan Li
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Bo Zhang
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Dingchu Zhang
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Fei Huang
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Guangyu Li
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Guoxin Chen
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Guoxin Chen
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Huifeng Yin
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Jialong Wu
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Jialong Wu
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Jingren Zhou
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Kuan Li
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Liangcai Su
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2025-10-29T12:45:58.344Z
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Liangcai Su
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Litu Ou
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Liwen Zhang
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Pengjun Xie
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Rui Ye
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RuiYe
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Wenbiao Yin
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Xinmiao Yu
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Xinyu Wang
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2025-10-30T14:42:27.908Z
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Wang Xinyu
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Xixi Wu
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Xixi Wu
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Xuanzhong Chen
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Xuanzhong Chen
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Yida Zhao
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Yida Zhao
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Zhen Zhang
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Zhengwei Tao
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Zhongwang Zhang
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Zile Qiao
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Chenxi Wang
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Donglei Yu
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Gang Fu
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Haiyang Shen
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Jiayin Yang
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Jun Lin
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Junkai Zhang
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Kui Zeng
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Li Yang
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Hailong Yin
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Maojia Song
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Ming Yan
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Peng Xia
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Peng Xia
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Qian Xiao
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Rui Min
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Ruixue Ding
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Runnan Fang
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Shaowei Chen
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Shen Huang
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Shihang Wang
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Shihao Cai
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Weizhou Shen
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Xiaobin Wang
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Xin Guan
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Xinyu Geng
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Yingcheng Shi
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Yuning Wu
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Zhuo Chen
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Zijian Li
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Yong Jiang
Tongyi DeepResearch Technical Report
We present Tongyi DeepResearch, an agentic large language model, which is
specifically designed for long-horizon, deep information-seeking research
tasks. To incentivize autonomous deep research agency, Tongyi DeepResearch is
developed through an end-to-end training framework that combines agentic
mid-training and agentic post-training, enabling scalable reasoning and
information seeking across complex tasks. We design a highly scalable data
synthesis pipeline that is fully automatic, without relying on costly human
annotation, and empowers all training stages. By constructing customized
environments for each stage, our system enables stable and consistent
interactions throughout. Tongyi DeepResearch, featuring 30.5 billion total
parameters, with only 3.3 billion activated per token, achieves
state-of-the-art performance across a range of agentic deep research
benchmarks, including Humanity's Last Exam, BrowseComp, BrowseComp-ZH,
WebWalkerQA, xbench-DeepSearch, FRAMES and xbench-DeepSearch-2510. We
open-source the model, framework, and complete solutions to empower the
community.
Tongyi DeepResearch, a large language model with agentic capabilities, achieves top performance in various deep research tasks through an end-to-end training framework and automatic data synthesis.
agentic large language model end-to-end training framework agentic mid-training agentic post-training scalable reasoning information seeking data synthesis pipeline customized environments Humanity's Last Exam BrowseComp BrowseComp-ZH WebWalkerQA xbench-DeepSearch FRAMES xbench-DeepSearch-2510