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charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:49:44.880333+00:00","bytes":2886,"raw_path":"bba45c2092dcabe53b5ca9083e7614d6924d62d10e2131660d212fc70fbd8a8a.md","content_hash":"77eef2de402a1e74d2009c1c76577b6fa1dcdc911edba49da99ff54c4faf6487","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: apache-2.0 base_model: - Qwen/Qwen3-4B language: - en tags: - agent - tool-use - reinforcement-learning - mcp --- <h1 align=\"center\">Arctic-AWM-14B</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 &nbsp; <sup>2</sup>Snowflake AI Research &nbsp; </p> Overview **Arctic-AWM-14B** is a multi-turn tool-use agent model trained with agentic reinforcement learning on [Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B), using the fully synthetic environments from..."},"evidence_pages":[{"url":"https://huggingface.co/Snowflake/Arctic-AWM-14B/raw/main/README.md","final_url":"https://huggingface.co/Snowflake/Arctic-AWM-14B/raw/main/README.md","title":"Snowflake/Arctic-AWM-14B model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:49:44.880333+00:00","bytes":2886,"raw_path":"bba45c2092dcabe53b5ca9083e7614d6924d62d10e2131660d212fc70fbd8a8a.md","content_hash":"77eef2de402a1e74d2009c1c76577b6fa1dcdc911edba49da99ff54c4faf6487","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: apache-2.0 base_model: - Qwen/Qwen3-4B language: - en tags: - agent - tool-use - reinforcement-learning - mcp --- <h1 align=\"center\">Arctic-AWM-14B</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 &nbsp; 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