Snowflake/Arctic-AWM-14B

Arctic-AWM-14B

Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning

Zhaoyang Wang1, Canwen Xu2, Boyi Liu2, Yite Wang2, Siwei Han1,

Zhewei Yao2, Huaxiu Yao1, Yuxiong He2

1UNC-Chapel Hill 2Snowflake AI Research

Overview

Arctic-AWM-14B is a multi-turn tool-use agent model trained with agentic reinforcement learning on Qwen3-14B, 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},
}