---
title: "Symbolic Learning Enables Self-Evolving Agents"
created: 2026-05-29
type: paper-raw
arxiv: "2406.18532"
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"]
venue: "arXiv preprint (cs.CL), June 2024"
affiliation: "AIWaves Inc."
tags: ["agent", "symbolic-learning", "self-evolving", "optimization"]
---

# Symbolic Learning Enables Self-Evolving Agents

**Authors:** Zhou et al. (AIWaves, 2024)
**arXiv:** [2406.18532](https://arxiv.org/abs/2406.18532)
**Code:** https://github.com/aiwaves-cn/agents

## Abstract

The AI community has been exploring a pathway to AGI by developing "language agents". A fundamental limitation is that current agent research is model-centric/engineering-centric — progress requires substantial manual engineering. Agent symbolic learning introduces a systematic framework that enables language agents to optimize themselves in a data-centric way using symbolic optimizers. Agents are considered as symbolic networks where learnable weights are defined by prompts, tools, and pipeline structure.

## Key Contributions

1. **Agent as Symbolic Network**: Pipeline = computation graph, Nodes = layers, Prompts/Tools = weights
2. **Symbolic Back-Propagation**: Language Loss propagated backward through the pipeline → Language Gradients for each node
3. **Holistic Joint Optimization**: All symbolic components optimized together, avoiding local optimum
4. **Self-Evolving**: Language Loss doesn't need ground-truth, enabling learning after deployment
