title: AgentGym: Evolving Large Language Model-based Agents across Diverse
    Environments
summary: Building generalist agents that can handle diverse tasks and evolve
  themselves across different environments is a long-term goal in the AI
  community. Large language models (LLMs) are considered a promising foundation
  to build such agents due to their generalized capabilities. Current approaches
  either have LLM-based agents imitate expert-provided trajectories step-by-step,
  requiring human supervision, which is hard to scale and limits environmental
  exploration; or they let agents explore and learn in isolated environments,
  resulting in specialist agents with limited generalization. In this paper, we
  take the first step towards building generally-capable LLM-based agents with
  self-evolution ability. We identify a trinity of ingredients: 1) diverse
  environments for agent exploration and learning, 2) a trajectory set to equip
  agents with basic capabilities and prior knowledge, and 3) an effective and
  scalable evolution method. We propose AgentGym, a new framework featuring a
  variety of environments and tasks for broad, real-time, uni-format, and
  concurrent agent exploration. AgentGym also includes a database with expanded
  instructions, a benchmark suite, and high-quality trajectories across
  environments. Next, we propose a novel method, AgentEvol, to investigate the
  potential of agent self-evolution beyond previously seen data across tasks and
  environments. Experimental results show that the evolved agents can achieve
  results comparable to SOTA models. We release the AgentGym suite, including the
  platform, dataset, benchmark, checkpoints, and algorithm implementations. The
  AgentGym suite is available on https://github.com/WooooDyy/AgentGym.
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paper_authors.name: Zhiheng Xi
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paper_authors.name: Wenxiang Chen
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paper_authors.name: Boyang Hong
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paper_authors.name: Honglin Guo
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paper_authors.fullname: Honglin Guo
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paper_authors._id: 666272df4de7d7f4dc428a38
paper_authors.name: Junzhe Wang
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paper_authors.fullname: Junzhe Wang
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paper_authors.user: jwang69
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paper_authors.name: Dingwen Yang
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paper_authors.name: Chenyang Liao
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paper_authors.fullname: Chenyang Liao
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paper_authors.name: Xin Guo
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paper_authors.fullname: Wei He
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paper_authors.name: Songyang Gao
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paper_authors.name: Lu Chen
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paper_authors.name: Rui Zheng
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paper_authors.name: Yicheng Zou
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paper_authors.name: Tao Gui
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paper_authors.name: Qi Zhang
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paper_authors.name: Xipeng Qiu
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paper_authors.fullname: Xipeng Qiu
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paper_authors.user: xpqiu
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paper_authors.name: Xuanjing Huang
paper_authors._id: 666272df4de7d7f4dc428a45
paper_authors.name: Zuxuan Wu
paper_authors._id: 666272df4de7d7f4dc428a46
paper_authors.name: Yu-Gang Jiang
paper_title: AgentGym: Evolving Large Language Model-based Agents across Diverse
    Environments
paper_summary: Building generalist agents that can handle diverse tasks and evolve
  themselves across different environments is a long-term goal in the AI
  community. Large language models (LLMs) are considered a promising foundation
  to build such agents due to their generalized capabilities. Current approaches
  either have LLM-based agents imitate expert-provided trajectories step-by-step,
  requiring human supervision, which is hard to scale and limits environmental
  exploration; or they let agents explore and learn in isolated environments,
  resulting in specialist agents with limited generalization. In this paper, we
  take the first step towards building generally-capable LLM-based agents with
  self-evolution ability. We identify a trinity of ingredients: 1) diverse
  environments for agent exploration and learning, 2) a trajectory set to equip
  agents with basic capabilities and prior knowledge, and 3) an effective and
  scalable evolution method. We propose AgentGym, a new framework featuring a
  variety of environments and tasks for broad, real-time, uni-format, and
  concurrent agent exploration. AgentGym also includes a database with expanded
  instructions, a benchmark suite, and high-quality trajectories across
  environments. Next, we propose a novel method, AgentEvol, to investigate the
  potential of agent self-evolution beyond previously seen data across tasks and
  environments. Experimental results show that the evolved agents can achieve
  results comparable to SOTA models. We release the AgentGym suite, including the
  platform, dataset, benchmark, checkpoints, and algorithm implementations. The
  AgentGym suite is available on https://github.com/WooooDyy/AgentGym.
paper_ai_keywords: AgentGym AgentEvol generalist agents LLM-based agents expert-provided trajectories environmental exploration specialist agents diverse environments trajectory set self-evolution uni-format concurrent exploration expanded instructions benchmark suite high-quality trajectories evolutionary method SOTA models