SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement

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 atthis https URL.

@article{fang2025_2504.03561,
  title={ SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement },
  author={ Runnan Fang and Xiaobin Wang and Yuan Liang and Shuofei Qiao and Jialong Wu and Zekun Xi and Ningyu Zhang and Yong Jiang and Pengjun Xie and Fei Huang and Huajun Chen },
  journal={arXiv preprint arXiv:2504.03561},
  year={ 2025 }
}