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Author: boyugou

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+- [UI‑TARS‑2 Technical Report: Advancing GUI Agent with Multi‑Turn Reinforcement Learning](https://arxiv.org/abs/2509.02544)
+    - Haoming Wang, Haoyang Zou, Huatong Song, Jiazhan Feng, Junjie Fang, Junting Lu, Longxiang Liu, Qinyu Luo, Shihao Liang, Shijue Huang, Wanjun Zhong, Yining Ye, Yujia Qin, Yuwen Xiong, Yuxin Song, Zhiyong Wu, Bo Li, Chen Dun, Chong Liu, Fuxing Leng, Hanbin Wang, Hao Yu, Haobin Chen, Hongyi Guo, Jing Su, Jingjia Huang, Kai Shen, Kaiyu Shi, Lin Yan, Peiyao Zhao, Pengfei Liu, Qinghao Ye, Renjie Zheng, Wayne Xin Zhao, Wen Heng, Wenhao Huang, Wenqian Wang, Xiaobo Qin, Yi Lin, Youbin Wu, Zehui Chen, Zihao Wang, Baoquan Zhong, Xinchun Zhang, Xujing Li, Yuanfan Li, Zhongkai Zhao, Chengquan Jiang, Faming Wu, Haotian Zhou, Jinlin Pang, Li Han, Qianli Ma, Siyao Liu, Songhua Cai, Wenqi Fu, Xin Liu, Zhi Zhang, Bo Zhou, Guoliang Li, Jiajun Shi, Jiale Yang, Jie Tang, Li Li, Taoran Lu, Woyu Lin, Xiaokang Tong, Xinyao Li, Yichi Zhang, Yu Miao, Zhengxuan Jiang, Zili Li, Ziyuan Zhao, Chenxin Li, Dehua Ma, Feng Lin, Ge Zhang, Haihua Yang, Hangyu Guo, Hongda Zhu, Jiaheng Liu, Junda Du, Kai Cai, Kuanye Li, Lichen Yuan, Meilan Han, Minchao Wang, Shuyue Guo, Tianhao Cheng, Xiaobo Ma, Xiaojun Xiao, Xiaolong Huang, Xinjie Chen, Yidi Du, Yilin Chen, Yiwen Wang, Zhaojian Li, Zhenzhu Yang, Zhiyuan Zeng, Chaolin Jin, Chen Li, Hao Chen, Haoli Chen, Jian Chen, Qinghao Zhao, Guang Shi
+    - 🏛️ Institutions: ByteDance Seed
+    - 📅 Date: September 2, 2025
+    - 📑 Publisher: arXiv
+    - 💻 Env: [GUI]
+    - 🔑 Key: [model], [reinforcement learning], [UI‑TARS‑2]
+    - 📖 TLDR: UI‑TARS‑2 is a newly trained native GUI‑centered agent that uses a scalable data flywheel, stabilized multi‑turn reinforcement learning, hybrid GUI + terminal environments, and a unified sandbox. It achieves leading performance across diverse GUI benchmarks (e.g., 88.2 on Online‑Mind2Web), game tasks (~60% human-level), and generalizes to information-seeking and software engineering tasks. Training dynamics and parameter interpolation offer insights into robust, efficient agent RL at scale.
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 - [Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training](https://arxiv.org/abs/2508.00414)
     - Tianqing Fang, Zhisong Zhang, Xiaoyang Wang, Rui Wang, Can Qin, Yuxuan Wan, Jun-Yu Ma, Ce Zhang, Jiaqi Chen, Xiyun Li, Hongming Zhang, Haitao Mi, Dong Yu
     - 🏛️ Institutions: Tencent AI Lab