Symbolic Learning Enables Self-Evolving Agents
(2406.18532)Abstract
The AI community has been exploring a pathway to artificial general intelligence (AGI) by developing "language agents", which are complex 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".
Overview
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The paper introduces a framework that shifts language agent research from an engineering-centric paradigm to a data-centric one, essential for progressing towards AGI.
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The framework leverages symbolic learning, treating language agents as symbolic networks with adjustable weights defined by prompts and tools, akin to neural networks, optimized using techniques derived from connectionist learning.
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Tested on standard benchmarks and complex tasks, the framework shows notable improvements in performance, with significant implications for both practical applications and theoretical advancements in AI.
An Expert Perspective on "Symbolic Learning Enables Self-Evolving Agents"
The paper "Symbolic Learning Enables Self-Evolving Agents," authored by Zhou et al., introduces an innovative framework designed to shift the current focus in language agent research from an engineering-centric paradigm to a data-centric paradigm. According to the authors, this transition is crucial for advancing towards AGI.
Conceptual Framework
This work introduces the concept of agent symbolic learning, a systematic framework for enabling language agents to optimize themselves in a data-centric way using symbolic optimizers. The key novelty lies in treating the language agent as a symbolic network where learnable weights are defined by prompts, tools, and their mutual integration—akin to the weights in a neural network. The authors leverage analogies from connectionist learning, particularly back-propagation and gradient descent, to propose a method where optimization operates over natural language simulacrums.
Methodology
Agent Pipeline Analogies:
- Agent Pipeline (A): Similar to a computational graph in neural networks, it represents the sequence of nodes through which input data is processed.
- Node (N): Analogous to a layer in a neural network, each node processes the input using prompts and tools, producing language-based outputs.
- Trajectory (τ): Akin to storing intermediate values in a computational graph, it keeps track of the inputs, prompts, tool usage, and outputs across nodes.
- Language Loss (L): A textual evaluation metric produced by a natural language loss function.
- Language Gradient (∇τ): Textual analyses and reflections that guide the updates to the symbolic components within the nodes.
Procedures:
- Forward Pass: Execution of the agent pipeline, storing relevant information in the trajectory.
- Language Loss Computation: Generation of language-based loss using a prompt that includes task description, input, trajectory, few-shot demonstrations, principles, and output format control.
- Back-propagation of Language Gradients: Iterative computation from the last to the first node to produce language gradients using another set of carefully designed prompts.
- Language Gradient-based Update: Updating prompts, tools, and the agent pipeline with symbolic optimizers.
Experimental Results
The framework was tested across standard language model benchmarks and more complex agentic tasks:
Standard Benchmarks:
- HotPotQA, MATH, and HumanEval results indicate that agent symbolic learning significantly improves performance metrics such as F1 scores and accuracy, outperforming both engineering-centric and other automated prompt optimization methods.
Complex Agentic Tasks:
- Creative Writing: The framework showed superior performance in creative writing, with language agents producing coherent and high-quality text as evaluated by modern LLM scoring models.
- Software Development: The framework effectively optimized the software development pipeline, producing projects with higher execution capability scores.
Implications
The proposed agent symbolic learning framework holds significant implications for both practical applications and theoretical developments. By enabling language agents to autonomously learn and evolve, the framework reduces the dependency on extensive manual engineering, making the development of versatile, task-general language agents more scalable.
Practical Implications
- Streamlining the creation and deployment of language agents in dynamic, real-world environments.
- Facilitating the development of self-improving systems capable of handling complex tasks such as software development and creative problem-solving.
Theoretical Implications
- Providing a new perspective on how symbolic reasoning and connectionist learning can be integrated to enhance agent learning.
- Paving the way for further exploration of unsupervised agent learning and self-evolution capabilities towards AGI.
Future Directions
The authors posit several future research avenues:
- Extending the framework to support joint optimization of symbolic and model-based learning, potentially including fine-tuning the neural backbone of agents.
- Designing robust benchmarks for evaluating agent learning in more complex, real-world scenarios.
- Investigating the potential of symbolic learning techniques in enhancing other AI subfields, like reinforcement learning or autonomous decision-making systems.
In conclusion, this paper presents a compelling advancement in the field of language agents, moving towards autonomous, self-evolving systems and setting a precedent for future research on the path to AGI.
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