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 at https://github.com/zjunlp/SynWorld.
67f351f068751b2bb84cc751
Runnan Fang
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Xiaobin Wang
67f351f068751b2bb84cc753
Yuan Liang
67f351f068751b2bb84cc754
Shuofei Qiao
67f351f068751b2bb84cc755
Jialong Wu
claimed_verified
2025-04-08T06:53:02.931Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/8k3b44MbhQiWuo6i8BnYl.jpeg
Jialong Wu
user
callanwu
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Zekun Xi
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Ningyu Zhang
claimed_verified
2025-05-21T12:27:16.942Z
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Ningyu Zhang
user
Ningyu
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Yong Jiang
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Pengjun Xie
67f351f068751b2bb84cc75a
Fei Huang
67f351f068751b2bb84cc75b
Huajun Chen
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 at https://github.com/zjunlp/SynWorld.
SynWorld framework enables agents to autonomously explore environments and refine action knowledge through scenario synthesis and Monte Carlo Tree Search.
LLM-based agents action space multi-step action invocation Monte Carlo Tree Search (MCTS)