Overview
Arctic-AWM-4B is a multi-turn tool-use agent model trained with agentic reinforcement learning on Qwen3-4B, using the fully synthetic environments from 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.
Resources
Related resources are also available, please check:
| Resource | Link |
|---|---|
| 📄 Paper | 📄 arxiv.org/abs/2602.10090 |
| 💻 Code | 💻 Snowflake-Labs/agent-world-model |
| 📦 AgentWorldModel-1K | 🤗 Snowflake/AgentWorldModel-1K |
| 🤖 Arctic-AWM-4B | 🤗 Snowflake/Arctic-AWM-4B |
| 🤖 Arctic-AWM-8B | 🤗 Snowflake/Arctic-AWM-8B |
| 🤖 Arctic-AWM-14B | 🤗 Snowflake/Arctic-AWM-14B |
Citation
If you find this resource useful, please kindly cite:
@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},
}