title: Symbolic Learning Enables Self-Evolving Agents
summary: The AI community has been exploring a pathway to artificial general
  intelligence (AGI) by developing "language agents", which are complex large
  language models (LLMs) pipelines involving both prompting techniques and tool
  usage methods. While language agents have demonstrated impressive capabilities
  for many real-world tasks, a fundamental limitation of current language agents
  research is that they are model-centric, or engineering-centric. That's to say,
  the progress on prompts, tools, and pipelines of language agents requires
  substantial manual engineering efforts from human experts rather than
  automatically learning from data. We believe the transition from model-centric,
  or engineering-centric, to data-centric, i.e., the ability of language agents
  to autonomously learn and evolve in environments, is the key for them to
  possibly achieve AGI.
    In this work, we introduce agent symbolic learning, a systematic framework
  that enables language agents to optimize themselves on their own in a
  data-centric way using symbolic optimizers. Specifically, we consider agents as
  symbolic networks where learnable weights are defined by prompts, tools, and
  the way they are stacked together. Agent symbolic learning is designed to
  optimize the symbolic network within language agents by mimicking two
  fundamental algorithms in connectionist learning: back-propagation and gradient
  descent. Instead of dealing with numeric weights, agent symbolic learning works
  with natural language simulacrums of weights, loss, and gradients. We conduct
  proof-of-concept experiments on both standard benchmarks and complex real-world
  tasks and show that agent symbolic learning enables language agents to update
  themselves after being created and deployed in the wild, resulting in
  "self-evolving agents".
paper_authors._id: 667d40359e4c6361d6a5a6f4
paper_authors.name: Wangchunshu Zhou
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paper_authors.name: Yixin Ou
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paper_authors.name: Shengwei Ding
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paper_authors.name: Long Li
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paper_authors.name: Jialong Wu
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paper_authors.fullname: Jialong Wu
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paper_authors.user: callanwu
paper_authors._id: 667d40359e4c6361d6a5a6f9
paper_authors.name: Tiannan Wang
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paper_authors.fullname: Tiannan Wang
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paper_authors.user: WTNswaggy
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paper_authors.name: Jiamin Chen
paper_authors._id: 667d40359e4c6361d6a5a6fb
paper_authors.name: Shuai Wang
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paper_authors._id: 668a17447d3f73951ed2ab84
paper_authors.avatarUrl: /avatars/2a1785af3b1b02e495304d2b118accb0.svg
paper_authors.fullname: Shuai Wang
paper_authors.type: user
paper_authors.user: Shuaiii
paper_authors._id: 667d40359e4c6361d6a5a6fc
paper_authors.name: Xiaohua Xu
paper_authors._id: 667d40359e4c6361d6a5a6fd
paper_authors.name: Ningyu Zhang
paper_authors.status: admin_assigned
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paper_authors.fullname: Ningyu Zhang
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paper_authors.user: Ningyu
paper_authors._id: 667d40359e4c6361d6a5a6fe
paper_authors.name: Huajun Chen
paper_authors._id: 667d40359e4c6361d6a5a6ff
paper_authors.name: Yuchen Eleanor Jiang
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paper_authors.fullname: Yuchen Eleanor Jiang
paper_authors.type: user
paper_authors.user: eleanorjiang
paper_title: Symbolic Learning Enables Self-Evolving Agents
paper_summary: The AI community has been exploring a pathway to artificial general
  intelligence (AGI) by developing "language agents", which are complex large
  language models (LLMs) pipelines involving both prompting techniques and tool
  usage methods. While language agents have demonstrated impressive capabilities
  for many real-world tasks, a fundamental limitation of current language agents
  research is that they are model-centric, or engineering-centric. That's to say,
  the progress on prompts, tools, and pipelines of language agents requires
  substantial manual engineering efforts from human experts rather than
  automatically learning from data. We believe the transition from model-centric,
  or engineering-centric, to data-centric, i.e., the ability of language agents
  to autonomously learn and evolve in environments, is the key for them to
  possibly achieve AGI.
    In this work, we introduce agent symbolic learning, a systematic framework
  that enables language agents to optimize themselves on their own in a
  data-centric way using symbolic optimizers. Specifically, we consider agents as
  symbolic networks where learnable weights are defined by prompts, tools, and
  the way they are stacked together. Agent symbolic learning is designed to
  optimize the symbolic network within language agents by mimicking two
  fundamental algorithms in connectionist learning: back-propagation and gradient
  descent. Instead of dealing with numeric weights, agent symbolic learning works
  with natural language simulacrums of weights, loss, and gradients. We conduct
  proof-of-concept experiments on both standard benchmarks and complex real-world
  tasks and show that agent symbolic learning enables language agents to update
  themselves after being created and deployed in the wild, resulting in
  "self-evolving agents".
paper_ai_keywords: agent symbolic learning symbolic network prompts tools back-propagation gradient descent simulacrum