Snowflake / Arctic-AWM-4B

Arctic-AWM-4B

Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning

Zhaoyang Wang ¹ , Canwen Xu ² , Boyi Liu ² , Yite Wang ² , Siwei Han ¹ ,
Zhewei Yao ² , Huaxiu Yao ¹ , Yuxiong He ²

¹ UNC-Chapel Hill ² Snowflake AI Research

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

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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}, 
}