SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866913869374423040 |
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| author | Fang, Runnan Wang, Xiaobin Liang, Yuan Qiao, Shuofei Wu, Jialong Xi, Zekun Zhang, Ningyu Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun |
| author_facet | Fang, Runnan Wang, Xiaobin Liang, Yuan Qiao, Shuofei Wu, Jialong Xi, Zekun Zhang, Ningyu Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun |
| contents | In the interaction between agents and their environments, agents expand their capabilities by planning and executing actions. However, LLM-based agents face substantial challenges when deployed in novel environments or required to navigate unconventional action spaces. To empower agents to autonomously explore environments, optimize workflows, and enhance their understanding of actions, we propose SynWorld, a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search (MCTS) exploration to effectively refine their action knowledge in the current environment. Our experiments demonstrate that SynWorld is an effective and general approach to learning action knowledge in new environments. Code is available at https://github.com/zjunlp/SynWorld. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement Fang, Runnan Wang, Xiaobin Liang, Yuan Qiao, Shuofei Wu, Jialong Xi, Zekun Zhang, Ningyu Jiang, Yong Xie, Pengjun Huang, Fei Chen, Huajun Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Multiagent Systems In the interaction between agents and their environments, agents expand their capabilities by planning and executing actions. However, LLM-based agents face substantial challenges when deployed in novel environments or required to navigate unconventional action spaces. To empower agents to autonomously explore environments, optimize workflows, and enhance their understanding of actions, we propose SynWorld, a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search (MCTS) exploration to effectively refine their action knowledge in the current environment. Our experiments demonstrate that SynWorld is an effective and general approach to learning action knowledge in new environments. Code is available at https://github.com/zjunlp/SynWorld. |
| title | SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2504.03561 |