Graphical User Interface (GUI) Agents, powered by multimodal large language models (MLLMs), have shown great potential for task automation on computing devices such as computers and mobile phones. However, existing agents face challenges in multi-step reasoning and reliance on textual annotations, limiting their effectiveness. We introduce \textit{InfiGUIAgent}, an MLLM-based GUI Agent trained with a two-stage supervised fine-tuning pipeline. Stage 1 enhances fundamental skills such as GUI understanding and grounding, while Stage 2 integrates hierarchical reasoning and expectation-reflection reasoning skills using synthesized data to enable native reasoning abilities of the agents. \textit{InfiGUIAgent} achieves competitive performance on several GUI benchmarks, highlighting the impact of native reasoning skills in enhancing GUI interaction for automation tasks. Resources are available at \url{https://github.com/Reallm-Labs/InfiGUIAgent}.

本研究解决了现有图形用户界面代理在多步推理和文本注释依赖方面的不足。通过引入InfiGUIAgent，这一基于多模态大型语言模型的代理采用了双阶段的监督微调流程，显著提高了代理的推理能力和GUI交互效果。研究结果表明，本地推理技能显著提升了自动化任务的性能。 

InfiGUIAgent：具有本地推理和反思能力的多模态通用GUI代理