Retrieval-augmented GUI Agents with Generative Guidelines

• URL: http://arxiv.org/abs/2509.24183v1
• Date: Mon, 29 Sep 2025 02:04:20 GMT
• Title: Retrieval-augmented GUI Agents with Generative Guidelines
• Authors: Ran Xu, Kaixin Ma, Wenhao Yu, Hongming Zhang, Joyce C. Ho, Carl Yang, Dong Yu,
• Abstract summary: We propose RAG-GUI, a lightweight VLM that leverages web tutorials at inference time.<n>RAG-GUI is first warm-started via supervised finetuning (SFT) and further refined through self-guided rejection sampling finetuning (RSF)<n>It consistently outperforms baseline agents and surpasses other inference baselines by 2.6% to 13.3% across two model sizes.
• Score: 45.75975553907856
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: GUI agents powered by vision-language models (VLMs) show promise in automating complex digital tasks. However, their effectiveness in real-world applications is often limited by scarce training data and the inherent complexity of these tasks, which frequently require long-tailed knowledge covering rare, unseen scenarios. We propose RAG-GUI , a lightweight VLM that leverages web tutorials at inference time. RAG-GUI is first warm-started via supervised finetuning (SFT) and further refined through self-guided rejection sampling finetuning (RSF). Designed to be model-agnostic, RAG-GUI functions as a generic plug-in that enhances any VLM-based agent. Evaluated across three distinct tasks, it consistently outperforms baseline agents and surpasses other inference baselines by 2.6% to 13.3% across two model sizes, demonstrating strong generalization and practical plug-and-play capabilities in real-world scenarios.

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