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README.md
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| Resource | Link |
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| π Paper | [π arxiv.org/abs/
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| π» Code | [π» Snowflake-Labs/agent-world-model](https://github.com/Snowflake-Labs/agent-world-model) |
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| π¦ AgentWorldModel-1K | [π€ Snowflake/AgentWorldModel-1K](https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K) |
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| π€ Arctic-AWM-4B | [π€ Snowflake/Arctic-AWM-4B](https://huggingface.co/Snowflake/Arctic-AWM-4B) |
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If you find this resource useful, please kindly cite:
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```bibtex
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```
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| Resource | Link |
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| π Paper | [π arxiv.org/abs/2602.10090](https://arxiv.org/abs/2602.10090) |
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| π» Code | [π» Snowflake-Labs/agent-world-model](https://github.com/Snowflake-Labs/agent-world-model) |
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| π¦ AgentWorldModel-1K | [π€ Snowflake/AgentWorldModel-1K](https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K) |
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| π€ Arctic-AWM-4B | [π€ Snowflake/Arctic-AWM-4B](https://huggingface.co/Snowflake/Arctic-AWM-4B) |
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If you find this resource useful, please kindly cite:
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```bibtex
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@article{wang2026agentworldmodelinfinity,
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title={Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning},
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author={Zhaoyang Wang and Canwen Xu and Boyi Liu and Yite Wang and Siwei Han and Zhewei Yao and Huaxiu Yao and Yuxiong He},
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year={2026},
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eprint={2602.10090},
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archivePrefix={arXiv},
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primaryClass={cs.AI},
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url={https://arxiv.org/abs/2602.10090},
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}
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```
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