DPO Learning with LLMs-Judge Signal for Computer Use Agents

• URL: http://arxiv.org/abs/2506.03095v1
• Date: Tue, 03 Jun 2025 17:27:04 GMT
• Title: DPO Learning with LLMs-Judge Signal for Computer Use Agents
• Authors: Man Luo, David Cobbley, Xin Su, Shachar Rosenman, Vasudev Lal, Shao-Yen Tseng, Phillip Howard,
• Abstract summary: Computer use agents (CUA) are systems that automatically interact with graphical user interfaces (GUIs) to complete tasks.<n>We develop a lightweight vision-language model that runs entirely on local machines.
• Score: 9.454381108993832
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computer use agents (CUA) are systems that automatically interact with graphical user interfaces (GUIs) to complete tasks. CUA have made significant progress with the advent of large vision-language models (VLMs). However, these agents typically rely on cloud-based inference with substantial compute demands, raising critical privacy and scalability concerns, especially when operating on personal devices. In this work, we take a step toward privacy-preserving and resource-efficient agents by developing a lightweight vision-language model that runs entirely on local machines. To train this compact agent, we introduce an LLM-as-Judge framework that automatically evaluates and filters synthetic interaction trajectories, producing high-quality data for reinforcement learning without human annotation. Experiments on the OS-World benchmark demonstrate that our fine-tuned local model outperforms existing baselines, highlighting a promising path toward private, efficient, and generalizable GUI agents.

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