58 lines
2.5 KiB
Markdown
58 lines
2.5 KiB
Markdown
---
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title: "Agent Symbolic Learning: 用符号学习实现自进化 Agent"
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created: 2026-05-29
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updated: 2026-05-29
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type: paper
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arxiv: "2406.18532"
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authors: ["Wangchunshu Zhou", "Yixin Ou", "Shengwei Ding", "Long Li", "Jialong Wu", "Tiannan Wang", "Jiamin Chen", "Shuai Wang", "Xiaohua Xu", "Ningyu Zhang", "Huajun Chen", "Yuchen Eleanor Jiang"]
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venue: "arXiv cs.CL, June 2024"
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tags: ["agent", "symbolic-learning", "self-evolving", "optimization"]
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sources: ["https://arxiv.org/abs/2406.18532"]
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---
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# Agent Symbolic Learning: 符号学习驱动的自进化 Agent
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> **论文**: Zhou et al. (AIWaves, 2024) — arXiv:2406.18532
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> **代码**: https://github.com/aiwaves-cn/agents
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## 核心问题
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当前 Agent 开发是 **engineering-centric** 的：prompt、工具、pipeline 都需要人类手动设计。Agent Symbolic Learning 提出了一个根本性转变——让 Agent **从数据中自动学习和进化**。
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## 方法：Agent = Symbolic Network
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| 神经网络 | Agent Symbolic Network |
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|----------|------|
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| 计算图 | Agent Pipeline |
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| 层 (Layer) | 节点 (Node) |
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| 权重 (Weights) | Prompts + Tools |
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| 损失函数 | [[language-loss\|Language Loss]] |
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| 梯度 | [[language-gradient\|Language Gradients]] |
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| 反向传播 | [[symbolic-backpropagation\|Symbolic Back-Propagation]] |
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| 优化器 | Symbolic Optimizer (LLM) |
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### 三阶段流程
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1. **Forward Pass**: Agent 沿 pipeline 执行 → 记录每个节点的轨迹
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2. **Backward Pass**: 从末节点向前传播 Language Loss → 每个节点的 Language Gradients
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3. **Weight Update**: Optimizer (LLM) 根据 gradients 更新所有 prompts/tools/pipeline
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## 关键创新
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- **Holistic Joint Optimization**: 同时优化所有符号组件，避免 DSPy 等方法分别优化带来的局部最优
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- **支持 pipeline 结构修改**: 不仅是改 prompt，还可以添加/删除节点
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- **无 ground-truth 也能学**: Language Loss 不需要标准答案
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## 历史定位
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这是"模仿神经网络反向传播来优化 Agent"思路的**原始提出者**。后续 [[yang-skillopt-2026|SkillOpt]]、[[heuristic-learning|Heuristic Learning]] 是在这一范式下的延伸和工程化。在吕明的两篇深度解读文章中被重点引用。
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## 概念网络
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- [[agent-symbolic-learning]] — 框架总览
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- [[symbolic-network]] — Agent 作为符号网络
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- [[language-gradient]] — 语言梯度
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- [[symbolic-backpropagation]] — 符号反向传播
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- [[self-evolving-agents]] — 自进化 Agent
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- [[language-loss]] — 语言损失
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