---
license: cc-by-nc-4.0
language:
- en
base_model:
- Qwen/Qwen3-VL-30B-A3B-Thinking
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- multimodal
- action
- agent
- pytorch
- computer use
- gui agents
---
# **Holo2: Foundational Models for Navigation and Computer Use Agents**
[](https://github.com/hcompai/hai-cookbook/tree/main/holo2)
## **Model Description**
**Holo2** represents the next major step in developing large-scale Vision-Language Models (VLMs) for **multi-domain GUI Agents**.
These agents can operate real digital environments specifically web, desktop, and mobile by interpreting interfaces, reasoning over content, and executing actions.
Our **Holo2** family emphasizes **navigation and task execution** across diverse real and simulated environments, extending beyond static perception to **multi-step, goal-directed behavior**.
It builds upon the strengths of **Holo1.5** in UI localization and screen content understanding, with major improvements in **policy learning**, **action grounding**, and **cross-environment generalization**.
The **Holo2** series comes in three model sizes:
- **Holo2-4B:** fully open under Apache 2.0
- **Holo2-8B:** fully open under Apache 2.0
- **Holo2-30B-A3B:** research-only license (non-commercial). For commercial use, please contact us.
These models are designed to provide reliable, accurate, and efficient foundations for next-generation CU agents, like Surfer-H.
- **Developed by:** [**H Company**](https://www.hcompany.ai/)
- **Model type:** Vision-Language Model for Navigation and Computer Use Agents
- **Fine-tuned from model:** Qwen/Qwen3-VL-30B-A3B-Thinking
- **Blog Post:** https://www.hcompany.ai/blog/holo2
- **License:** Apache 2.0 License
## Get Started with the Model
Please have a look at the [cookbook](https://github.com/hcompai/hai-cookbook/tree/main/holo2) in our repo where we provide examples for both self-hosting and API use!
## **Training Strategy**
Our models are trained using high-quality proprietary data for UI understanding and action prediction, following a multi-stage training pipeline. The training dataset is a carefully curated mix of open-source datasets, large-scale synthetic data, and human-annotated samples. Training proceeds in two stages: large-scale supervised fine-tuning, followed by online reinforcement learning (GRPO) yielding SOTA performance in interpreting UIs and performing actions on large, complex screens
## **Results**
### **Holo2: Navigation Performance**
Navigation evaluates an agent’s ability to complete real or simulated tasks through multi-step reasoning and action.
Holo2 models show significant improvements in navigation efficiency and task completion rates, particularly in unseen and complex environments.
Benchmarks include **WebVoyager**, **WebArena**, **OSWorld**, and **AndroidWorld**, testing the models’ abilities across web, operating system, and mobile platforms.
| Model | WebVoyager | WebArena | OSWorld | AndroidWorld | Average |
|---------------------------|------------|----------|---------|---------------|---------|
| Holo2-30B-A3B | **83.0%** | **46.3%**| 37.4% |**71.6%** | **59.6%**|
| Holo2-8B | 80.2% | 42.2% |**39.9%**| 60.4% | 55.7% |
| Holo2-4B | 80.2% | 41.0% | 37.7% | 64.6% | 55.9% |
| Holo1.5-7B | 65.9% | 23.4% | 6.4% | 32.7% | 32.1% |
| Holo1.5-3B | 56.1% | 15.4% | 5.8% | 27.5% | 26.2% |
| Qwen3-VL-30B-A3B-Thinking | 76.1% | 45.0% | 36.6% | 62.9% | 55.1% |
| Qwen3-VL-8B-Thinking | 72.0% | 31.9% | 28.8% | 52.6% | 46.3% |
| Qwen3-VL-4B-Thinking | 67.5% | 31.5% | 24.1% | 45.7% | 42.2% |
Table 1: Navigation benchmark scores. Bold values will denote state-of-the-art once final evaluations are available.
All external model scores are reproduced internally in the Surfer 2 agent, to allow for fair comparison
---
### **Holo2: SOTA UI Localization**
UI Localization measures how precisely an agent can locate on-screen elements—buttons, inputs, links—necessary for accurate interaction.
Holo2 continues to set new standards for localization accuracy across web, OS, and mobile benchmarks.
| | ScreenSpot-Pro | OSWorld-G | Showdown | Ground-UI-1K | WebClick-v1 | ScreenSpot-v2 | Average |
|---------------------------|----------------|-----------|----------|--------------|-------------|---------------|----------|
| Holo2-30B-A3B | **66.1%** | **76.1%** | **77.6%**| **85.4%** | 91.3% | 94.9% | **81.90**|
| Holo2-8B | 58.9% | 70.1% | 72.5% | 83.8% | 89.5% | 93.2% | 78.00 |
| Holo2-4B | 57.2% | 69.4% | 74.7% | 83.3% | 88.8% | 93.2% | 77.77 |
| Holo1.5-72B | 63.3% | 71.8% | 76.8% | 84.5% | **92.4%** | 94.4% | 80.52 |
| Holo1.5-7B | 57.9% | 66.2% | 72.1% | 84.0% | 90.2% | 93.3% | 77.28 |
| Holo1.5-3B | 51.4% | 61.5% | 67.5% | 83.2% | 81.4% | 91.6% | 72.77 |
| Qwen3-VL-30B-A3B-Thinking | 49.9% | 65.8% | 71.2% | 84.2% | 89.5% | 91.8% | 75.40 |
| Qwen3-VL-8B-Thinking | 38.5% | 56.0% | 64.2% | 83.6% | 85.9% | 91.5% | 69.95 |
| Qwen3-VL-4B-Thinking | 41.4% | 56.4% | 66.6% | 84.1% | 85.8% | 90.0% | 70.72 |
| Qwen2.5-VL-72B | 55.6% | 62.0% | 41.0% | 85.4% | 88.3% | 93.3% | 70.93 |
| Qwen2.5-VL-7B | 29.0% | 40.6% | 52.0% | 80.7% | 76.5% | 85.6% | 60.73 |
| Qwen2.5-VL-3B | 29.3% | 34.3% | 50.3% | 76.4% | 71.2% | 80.7% | 57.03 |
| UI-TARS-1.5-7B | 39.0% | 61.0% | 58.0% | 84.0% | 86.1% | 94.0% | 70.35 |
| UI-Venus-72B | 61.9% | 70.4% | 75.6% | 75.5% | 77.0% | **95.3%** | 75.95 |
| UI-Venus-7B | 50.8% | 58.8% | 67.3% | 82.3% | 84.4% | 94.1% | 72.95 |
Table 2: Localization benchmark scores for leading models.
Accuracy of our and competitors' models on UI Localization benchmarks.
---
## Citation
```bibtex
@misc{hai2025holo2modelfamily,
title={Holo2 - Open Foundation Models for Navigation and Computer Use Agents},
author={H Company},
year={2025},
url=https://huggingface.co/collections/Hcompany/holo2,
}