Symbolic Learning Enables Self-Evolving Agents
The AI community has been exploring a pathway to artificial general intelligence (AGI) by developing “language agents”, which are complex large language models (LLMs) workflows 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 is to say, the design of prompts, tools, and workflows 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 in which 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 in a data-centric way by mimicking two fundamental algorithms in connectionist learning: back-propagation and gradient descent. Instead of dealing with numeric weights, agent symbolic learning works with text-based weights, loss, and gradients. We conduct proof-of-concept experiments on both standard benchmarks and complex real-world tasks and show substantial improvements over static agent frameworks and simple prompt/tool optimization methods. In addition, agent symbolic learning enables language agents to update themselves after being created and deployed in the wild, resulting in “self-evolving agents”. We will open-source the agent symbolic learning framework to facilitate future research on data-centric agent learning.