chakra-labs/GLADOS-1

GLADOS-1 — UI-TARS-7B-SFT

Model Description

GLADOS-1 is the first computer-use (CUA) model post-trained using collective, crowd-sourced trajectories. Leveraging the enourmous PANGO dataset (with primarily Chrome based interactions), it's purpose is to provide a lense as to what's possible with enormous trajectory sizes in computer use.

It also represents the first open-sourced post-training pipeline for UI-TARS, inspired by the existing Qwen2VL finetuning series.

This model is designed to:

• —Be compliant. It has been taught to rigorouly follow directions and output action formats compatible with downstream parsers like PyAutoGUI.
• —Understand web productivity applications. The Pango dataset primarily contains productivity application usage in browser. Consequently in OSWorld results, we observe significantly improved performance on the Chrome task bench.
• —Have strong intuition on visual grounding. Our experiments are detailed more closely here in our research blog.

<div align="left"> <p> 📕 <a href="https://www.chakra.dev/research/glados-1-compute-use-model-crowdsourced-trajectories">Release Blog</a>&nbsp&nbsp | &nbsp&nbsp 🤗 <a href="https://github.com/Chakra-Network/GLADOS-1">Code</a>&nbsp&nbsp | &nbsp&nbsp 🔧 <a href="https://github.com/bytedance/UI-TARS/blob/main/README_deploy.md">Deployment (via UI-TARS)</a> &nbsp&nbsp | &nbsp&nbsp 🖥️ <a href="https://github.com/bytedance/UI-TARS-desktop">Running on your own computer (via UI-TARS Desktop)</a>&nbsp&nbsp </p> </div>

Citation

@misc{chakralabs2025glados-1,
  author = {Chakra Labs},
  title = {GLADOS-1},
  url = {https://github.com/Chakra-Network/GLADOS-1},
  year = {2025}
}