字节Seed最新版原生智能体来了！一个模型搞定手机/电脑/浏览器自主操作

Core Viewpoint - The article discusses the advancements of ByteDance's UI-TARS-2, a new generation of AI agents that can autonomously operate graphical user interfaces (GUIs) across various platforms, outperforming competitors like Claude and OpenAI [2][23][24]. Group 1: UI-TARS-2 Overview - UI-TARS-2 is designed to autonomously complete complex tasks on computers, mobile devices, web browsers, terminals, and even games [6][10]. - The architecture includes a unified agent framework, multimodal perception, multi-round reinforcement learning, and hybrid operation flows [7][8]. Group 2: Challenges Addressed - UI-TARS-2 tackles four major challenges in AI GUI operation: data scarcity, environment fragmentation, single capability, and training instability [5][10]. - The model employs a "data flywheel" strategy to address data scarcity by collecting raw data and generating high-quality task-specific data through iterative training [11][12]. Group 3: Reinforcement Learning Enhancements - The team optimized traditional reinforcement learning methods to ensure stable operations in long-duration GUI tasks by improving task design, reward mechanisms, and training processes [15][17]. - The model uses asynchronous rollout and several enhancements to the PPO algorithm to improve stability and encourage exploration of less common but potentially effective actions [17][18]. Group 4: Performance Metrics - UI-TARS-2 has shown superior performance in various GUI tests, scoring higher than Claude and OpenAI models in tasks across different operating systems and command-line environments [23][24]. - In gaming scenarios, UI-TARS-2 achieved an average score of approximately 60% of human performance, outperforming competitors in several games [27][28]. Group 5: Practical Applications - Beyond GUI operations, UI-TARS-2 can perform tasks such as information retrieval and code debugging, demonstrating its versatility and effectiveness compared to models relying solely on GUI interactions [28][29].