UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn
  Reinforcement Learning
The development of autonomous agents for graphical user interfaces (GUIs)
presents major challenges in artificial intelligence. While recent advances in
native agent models have shown promise by unifying perception, reasoning,
action, and memory through end-to-end learning, open problems remain in data
scalability, multi-turn reinforcement learning (RL), the limitations of
GUI-only operation, and environment stability. In this technical report, we
present UI-TARS-2, a native GUI-centered agent model that addresses these
challenges through a systematic training methodology: a data flywheel for
scalable data generation, a stabilized multi-turn RL framework, a hybrid GUI
environment that integrates file systems and terminals, and a unified sandbox
platform for large-scale rollouts. Empirical evaluation demonstrates that
UI-TARS-2 achieves significant improvements over its predecessor UI-TARS-1.5.
On GUI benchmarks, it reaches 88.2 on Online-Mind2Web, 47.5 on OSWorld, 50.6 on
WindowsAgentArena, and 73.3 on AndroidWorld, outperforming strong baselines
such as Claude and OpenAI agents. In game environments, it attains a mean
normalized score of 59.8 across a 15-game suite-roughly 60% of human-level
performance-and remains competitive with frontier proprietary models (e.g.,
OpenAI o3) on LMGame-Bench. Additionally, the model can generalize to
long-horizon information-seeking tasks and software engineering benchmarks,
highlighting its robustness across diverse agent tasks. Detailed analyses of
training dynamics further provide insights into achieving stability and
efficiency in large-scale agent RL. These results underscore UI-TARS-2's
potential to advance the state of GUI agents and exhibit strong generalization
to real-world interactive scenarios.
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Haoming Wang
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Haoyang Zou
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Huatong Song
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Jiazhan Feng
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Junjie Fang
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Junting Lu
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Longxiang Liu
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Qinyu Luo
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Shihao Liang
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Shijue Huang
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Shijue Huang
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Wanjun Zhong
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Yining Ye
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Yuwen Xiong
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Yuxin Song
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Zhiyong Wu
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Bo Li
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Chen Dun
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Chong Liu
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Hao Yu
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Haobin Chen
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Hongyi Guo
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Jing Su
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Jingjia Huang
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Kai Shen
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Kaiyu Shi
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Lin Yan
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Peiyao Zhao
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Pengfei Liu
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Qinghao Ye
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Renjie Zheng
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Wayne Xin Zhao
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Wen Heng
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Xiaobo Qin
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Yi Lin
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Youbin Wu
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Zehui Chen
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Zihao Wang
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Haotian Zhou
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Jinlin Pang
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Songhua Cai
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Xin Liu
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Zhi Zhang
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Bo Zhou
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Jiale Yang
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Jie Tang
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Li Li
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Taoran Lu
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Yichi Zhang
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Catill Zhang
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Ge Zhang
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Hangyu Guo
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Hongda Zhu
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Jiaheng Liu
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Junda Du
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Kuanye Li
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Lichen Yuan
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Xiaolong Huang
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Yidi Du
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Yilin Chen
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Yiwen Wang
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Zhaojian Li
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Zhenzhu Yang
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Zhiyuan Zeng
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Chen Li
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Hao Chen
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Haoli Chen
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Guang Shi
UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn
  Reinforcement Learning
The development of autonomous agents for graphical user interfaces (GUIs)
presents major challenges in artificial intelligence. While recent advances in
native agent models have shown promise by unifying perception, reasoning,
action, and memory through end-to-end learning, open problems remain in data
scalability, multi-turn reinforcement learning (RL), the limitations of
GUI-only operation, and environment stability. In this technical report, we
present UI-TARS-2, a native GUI-centered agent model that addresses these
challenges through a systematic training methodology: a data flywheel for
scalable data generation, a stabilized multi-turn RL framework, a hybrid GUI
environment that integrates file systems and terminals, and a unified sandbox
platform for large-scale rollouts. Empirical evaluation demonstrates that
UI-TARS-2 achieves significant improvements over its predecessor UI-TARS-1.5.
On GUI benchmarks, it reaches 88.2 on Online-Mind2Web, 47.5 on OSWorld, 50.6 on
WindowsAgentArena, and 73.3 on AndroidWorld, outperforming strong baselines
such as Claude and OpenAI agents. In game environments, it attains a mean
normalized score of 59.8 across a 15-game suite-roughly 60% of human-level
performance-and remains competitive with frontier proprietary models (e.g.,
OpenAI o3) on LMGame-Bench. Additionally, the model can generalize to
long-horizon information-seeking tasks and software engineering benchmarks,
highlighting its robustness across diverse agent tasks. Detailed analyses of
training dynamics further provide insights into achieving stability and
efficiency in large-scale agent RL. These results underscore UI-TARS-2's
potential to advance the state of GUI agents and exhibit strong generalization
to real-world interactive scenarios.
UI-TARS-2, a native GUI-centered agent model, addresses challenges in data scalability, multi-turn reinforcement learning, and environment stability, achieving significant improvements over its predecessor and strong baselines across various benchmarks.
reinforcement learning GUI data flywheel multi-turn RL hybrid GUI environment unified sandbox platform Online-Mind2Web OSWorld WindowsAgentArena AndroidWorld LMGame-Bench long-horizon information-seeking tasks software engineering benchmarks