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}.
@article{arxiv.2501.04575,
title = {InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection},
author = {Yuhang Liu and Pengxiang Li and Zishu Wei and Congkai Xie and Xueyu Hu and Xinchen Xu and Shengyu Zhang and Xiaotian Han and Hongxia Yang and Fei Wu},
journal= {arXiv preprint arXiv:2501.04575},
year = {2025}
}