💡 The paper presents UI-TARS-2, a native GUI-centered agent model that addresses challenges in data scalability, multi-turn reinforcement learning, and environment stability. The development of autonomous agents for graphical user interfaces is a major challenge in artificial intelligence, with open problems in data scalability, multi-turn reinforcement learning, and environment stability. To address these challenges, the authors propose a systematic training methodology that includes a data flywheel for scalable data generation, a stabilized multi-turn reinforcement learning framework, a hybrid GUI environment that integrates file systems and terminals, and a unified sandbox platform for large-scale rollouts.

The authors evaluate UI-TARS-2 on various benchmarks, including GUI benchmarks such as Online-Mind2Web, OSWorld, WindowsAgentArena, and AndroidWorld, as well as game environments and software engineering benchmarks. The results show that UI-TARS-2 achieves significant improvements over its predecessor UI-TARS-1.5 and strong baselines such as Claude and OpenAI agents. Specifically, UI-TARS-2 reaches high scores on GUI benchmarks, attains a mean normalized score of 59.8 across a 15-game suite, and remains competitive with frontier proprietary models on LMGame-Bench.

The model also generalizes to long-horizon information-seeking tasks and software engineering benchmarks, highlighting its robustness across diverse agent tasks. The authors provide detailed analyses of training dynamics, which provide insights into achieving stability and efficiency in large-scale agent reinforcement learning. Overall, the paper demonstrates UI-TARS-2's potential to advance the state of GUI agents and exhibit strong generalization to real-world interactive scenarios. The contributions of the paper include the development of a systematic training methodology, the evaluation of UI-TARS-2 on various benchmarks, and the analysis of training dynamics, which provide insights into achieving stability and efficiency in large-scale agent reinforcement learning.