ScaleCUA-glm4.6v-osworld
A GLM-4.6V-Flash computer-use agent from ScaleCUA, trained for OSWorld. Stage: online-RL (RLVR).
This model was introduced in the paper SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL.
- 📄 Paper / code: https://github.com/THUDM/SCALE-CUA
- 🤗 Collection: https://huggingface.co/collections/extreme1228/scalecua-6a4cad5e3c80a230622eaff3 · 🤖 ModelScope
What is this
This is the online-RL checkpoint (the released agent). Its SFT start point is extreme1228/ScaleCUA-glm4.6v-osworld-sft. It reaches 66.5% on OSWorld.
ScaleCUA scales online RL for computer-use agents by pairing VeriGen (verifiable GUI-task synthesis) with Frontier Sampling and Visual Context Segmentation. See the repo for details.
Usage
Serve with an OpenAI-compatible server (e.g. vLLM) and drive it with the
evaluation runtime in osworld_eval:
pip install -U vllm
python -m vllm.entrypoints.openai.api_server --model extreme1228/ScaleCUA-glm4.6v-osworld \
--served-model-name scalecua --trust-remote-code --port 8000
Then follow REPRODUCE.md to run OSWorld evaluation.
Results
66.5% on OSWorld (see the paper for the full table).
License
Weights are released for research; the model inherits the license of its base model (GLM-4.6V-Flash). Repository code is under the licenses in the GitHub repo.
Citation
@misc{lv2026scalecua,
title = {ScaleCUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL},
author = {Bowen Lv and Xiao Liu and Yanyu Ren and Hanyu Lai and Bohao Jing and Hanchen Zhang and Yanxiao Zhao and Shuntian Yao and Jie Tang and Yuxiao Dong},
year = {2026},
url = {https://github.com/THUDM/SCALE-CUA}
}