OSExpert: Computer-Use Agents Learning Professional Skills via Exploration
Jiateng Liu, Zhenhailong Wang, Rushi Wang, Bingxuan Li, Jeonghwan Kim, Aditi Tiwari, Pengfei Yu, Denghui Zhang, Heng Ji

TL;DR
This paper introduces OSExpert, a learning agent for computer use tasks that employs a novel exploration algorithm and skill composition to improve performance and efficiency, approaching human expert levels.
Contribution
The paper presents a GUI-based exploration algorithm and a skill self-construction method that significantly enhance agent performance in complex digital tasks.
Findings
Achieves 20% performance improvement on OSExpert-Eval
Reduces inference-time scaling inefficiency by 80%
Demonstrates effective skill transfer and compositionality
Abstract
General-purpose computer-use agents have shown impressive performance across diverse digital environments. However, our new benchmark, OSExpert-Eval, indicates they remain far less helpful than human experts. Although inference-time scaling enables adaptation, these agents complete complex tasks inefficiently with degraded performance, transfer poorly to unseen UIs, and struggle with fine-grained action sequences. To solve the problem, we introduce a GUI-based depth-first search (GUI-DFS) exploration algorithm to comprehensively explore and verify an environment's unit functions. The agent then exploits compositionality between unit skills to self-construct a curriculum for composite tasks. To support fine-grained actions, we curate a database of action primitives for agents to discover during exploration; these are saved as a skill set once the exploration is complete. We use the…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Explainable Artificial Intelligence (XAI) · Intelligent Tutoring Systems and Adaptive Learning
