title: SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge
    Refinement
summary: 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.
paper_authors._id: 67f351f068751b2bb84cc751
paper_authors.name: Runnan Fang
paper_authors._id: 67f351f068751b2bb84cc752
paper_authors.name: Xiaobin Wang
paper_authors._id: 67f351f068751b2bb84cc753
paper_authors.name: Yuan Liang
paper_authors._id: 67f351f068751b2bb84cc754
paper_authors.name: Shuofei Qiao
paper_authors._id: 67f351f068751b2bb84cc755
paper_authors.name: Jialong Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:53:02.931Z
paper_authors._id: 644a4fbc2166258fccc664bc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/8k3b44MbhQiWuo6i8BnYl.jpeg
paper_authors.fullname: Jialong Wu
paper_authors.type: user
paper_authors.user: callanwu
paper_authors._id: 67f351f068751b2bb84cc756
paper_authors.name: Zekun Xi
paper_authors._id: 67f351f068751b2bb84cc757
paper_authors.name: Ningyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T12:27:16.942Z
paper_authors._id: 620b3bbb0668e435407c8d0a
paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_authors._id: 67f351f068751b2bb84cc758
paper_authors.name: Yong Jiang
paper_authors._id: 67f351f068751b2bb84cc759
paper_authors.name: Pengjun Xie
paper_authors._id: 67f351f068751b2bb84cc75a
paper_authors.name: Fei Huang
paper_authors._id: 67f351f068751b2bb84cc75b
paper_authors.name: Huajun Chen
paper_title: SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge
    Refinement
paper_summary: 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.
paper_ai_summary: SynWorld framework enables agents to autonomously explore environments and refine action knowledge through scenario synthesis and Monte Carlo Tree Search.
paper_ai_keywords: LLM-based agents action space multi-step action invocation Monte Carlo Tree Search (MCTS)