File size: 2,882 Bytes
8f2efdc ba24c53 437dfa0 ba24c53 437dfa0 ba24c53 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
license: apache-2.0
base_model:
- Qwen/Qwen3-4B
language:
- en
tags:
- agent
- tool-use
- reinforcement-learning
- mcp
---
<h1 align="center">Arctic-AWM-4B</h1>
<h3 align="center">Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning</h3>
<p align="center">
<a href="https://github.com/Raibows">Zhaoyang Wang<sup>1</sup></a>,
<a href="https://www.canwenxu.net/">Canwen Xu<sup>2</sup></a>,
<a href="https://www.snowflake.com/en/blog/authors/boyi-liu/">Boyi Liu<sup>2</sup></a>,
<a href="https://yitewang.github.io/">Yite Wang<sup>2</sup></a>,
<a href="https://lillianwei-h.github.io/">Siwei Han<sup>1</sup></a>,<br/>
<a href="https://yaozhewei.github.io/">Zhewei Yao<sup>2</sup></a>,
<a href="https://www.huaxiuyao.io/">Huaxiu Yao<sup>1</sup></a>,
<a href="https://www.snowflake.com/en/blog/authors/yuxiong-he/">Yuxiong He<sup>2</sup></a>
</p>
<p align="center">
<sup>1</sup>UNC-Chapel Hill <sup>2</sup>Snowflake AI Research
</p>
# Overview
**Arctic-AWM-4B** is a multi-turn tool-use agent model trained with agentic reinforcement learning on [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B), using the fully synthetic environments from [AgentWorldModel-1K](https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K).
The model is trained to interact with tool-use environments exposed via a unified MCP (Model Context Protocol) interface, enabling strong multi-turn agentic capabilities.
For detailed usage of the model, please visit [https://github.com/Snowflake-Labs/agent-world-model](https://github.com/Snowflake-Labs/agent-world-model).
# Resources
Related resources are also available, please check:
| Resource | Link |
|----------|------|
| 📄 Paper | [📄 arxiv.org/abs/2602.10090](https://arxiv.org/abs/2602.10090) |
| 💻 Code | [💻 Snowflake-Labs/agent-world-model](https://github.com/Snowflake-Labs/agent-world-model) |
| 📦 AgentWorldModel-1K | [🤗 Snowflake/AgentWorldModel-1K](https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K) |
| 🤖 Arctic-AWM-4B | [🤗 Snowflake/Arctic-AWM-4B](https://huggingface.co/Snowflake/Arctic-AWM-4B) |
| 🤖 Arctic-AWM-8B | [🤗 Snowflake/Arctic-AWM-8B](https://huggingface.co/Snowflake/Arctic-AWM-8B) |
| 🤖 Arctic-AWM-14B | [🤗 Snowflake/Arctic-AWM-14B](https://huggingface.co/Snowflake/Arctic-AWM-14B) |
# Citation
If you find this resource useful, please kindly cite:
```bibtex
@article{wang2026agentworldmodelinfinity,
title={Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning},
author={Zhaoyang Wang and Canwen Xu and Boyi Liu and Yite Wang and Siwei Han and Zhewei Yao and Huaxiu Yao and Yuxiong He},
year={2026},
eprint={2602.10090},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2602.10090},
}
```
|