---
title: "ToolCUA: Towards Optimal GUI-Tool Path Orchestration for Computer Use Agents"
created: 2026-05-12
type: paper
source: https://arxiv.org/abs/2605.12481
code: https://github.com/X-PLUG/ToolCUA
authors:
  - Xuhao Hu (Fudan)
  - Xi Zhang (Alibaba)
  - Haiyang Xu (Alibaba)
  - Kyle Qiao (Alibaba)
  - Jingyi Yang (Fudan)
  - Xuanjing Huang (Fudan)
  - Jing Shao (Shanghai AI Lab)
  - Ming Yan (Alibaba)
  - Jieping Ye (Alibaba)
venue: arXiv:2605.12481, 2026
tags: [computer-use-agents, gui-tool-orchestration, reinforcement-learning, trajectory-optimization]
---

# ToolCUA: Towards Optimal GUI-Tool Path Orchestration for Computer Use Agents

**Authors**: Xuhao Hu, Xi Zhang, Haiyang Xu, Kyle Qiao, Jingyi Yang, Xuanjing Huang, Jing Shao, Ming Yan, Jieping Ye

**Affiliations**: Tongyi Lab, Alibaba Group; Fudan University; Shanghai Artificial Intelligence Laboratory

**arXiv**: 2605.12481 | **Date**: May 12, 2026 | **Code**: https://github.com/X-PLUG/ToolCUA

## Abstract

Computer Use Agents (CUAs) can act through both atomic GUI actions, such as click and type, and high-level tool calls, such as API-based file operations, but this hybrid action space often leaves them uncertain about when to continue with GUI actions or switch to tools, leading to suboptimal execution paths. This difficulty stems from the scarcity of high-quality interleaved GUI-Tool trajectories, the cost and brittleness of collecting real tool trajectories, and the lack of trajectory-level supervision for GUI-Tool path selection. In this paper, we propose ToolCUA, an end-to-end agent designed to learn optimal GUI-Tool path selection through a staged training paradigm. We first introduce an Interleaved GUI-Tool Trajectory Scaling Pipeline that repurposes abundant static GUI trajectories and synthesizes a grounded tool library, enabling diverse GUI-Tool trajectories without manual engineering or real tool-trajectory collection. We then perform Tool-Bootstrapped GUI RFT, combining warmup SFT with single-turn RL to improve decisions at critical GUI-Tool switching points. Finally, we optimize ToolCUA with Online Agentic RL in a high-fidelity GUI-Tool environment, guided by a Tool-Efficient Path Reward that encourages appropriate tool use and shorter execution paths. Experiments on OSWorld-MCP show that ToolCUA achieves 46.85% accuracy, a relative improvement of approximately 66% over the baseline, establishing a new state of the art among models of comparable scale.

## Key Concepts

- [[computer-use-agents|Computer Use Agents (CUAs)]]
- [[gui-tool-hybrid-action-space|GUI-Tool Hybrid Action Space]]
- [[optimal-gui-tool-path-selection]]
- [[interleaved-gui-tool-trajectory-scaling]]
- [[tool-bootstrapped-rft]]
- [[tool-efficient-path-reward]]
- [[osworld-mcp]]
- [[next-state-grounding]]
- [[grpo]]
- [[agent-computer-interface]]
