InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection
This addresses the challenge of task automation on computing devices for users by enhancing GUI interaction through native reasoning, though it appears incremental as it builds on existing MLLM-based agents.
The paper tackles the problem of GUI agents struggling with multi-step reasoning and reliance on textual annotations by introducing InfiGUIAgent, a multimodal GUI agent trained with a two-stage fine-tuning pipeline that integrates hierarchical and expectation-reflection reasoning, achieving competitive performance on GUI benchmarks.
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}.