Large-scale language models (LLMs) have revolutionized the field of artificial intelligence, enabling the creation of language agents that can solve complex tasks autonomously. However, there are significant challenges in developing these agents. Current approaches involve manually decomposing tasks into LLM pipelines and stacking prompts and tools. This process is labor-intensive and engineering-centric, limiting the adaptability and robustness of language agents. The complexity of this manual customization makes it nearly impossible to optimize language agents for diverse datasets in a data-centric manner, hindering their generalizability and applicability to new tasks and data distributions. Researchers are currently exploring ways to move from this engineering-centric approach to a more data-centric learning paradigm for language agent development.
Previous research has attempted to address the challenge of language agent optimization through automated prompt engineering and agent optimization techniques. These approaches fall into two categories: prompt-based and search-based. Prompt-based methods optimize specific components in the agent pipeline, while search-based approaches find optimal prompts or nodes in the combinatorial space. However, these methods have limitations, including difficulty with complex real-world tasks and a tendency to local optima. They also fail to holistically optimize the entire agent system. Other research directions, such as synthesizing data for fine-tuning LLMs and exploring cross-task transfer learning, are promising but do not fully address the need for comprehensive agent system optimization.
Introduced by researchers from AIWaves Inc. Agent Symbolic Learning Framework As an innovative approach to training language agents, inspired by neural network learning. Drawing analogies between language agents and neural nets, the framework maps agent pipelines to computational graphs, nodes to layers, and prompts and tools to weights. It maps agent components to neural network elements, enabling a process similar to backpropagation. The framework runs agents and evaluates their performance using a “linguistic loss” to generate “linguistic gradients” through backpropagation. These gradients guide a comprehensive optimization of all symbolic components, including prompts, tools, and the overall pipeline structure. This approach avoids local optima, enables effective learning of complex tasks, and supports multi-agent systems. It allows for self-evolution of agents after deployment, potentially shifting language agent research from an engineering-centric to a data-centric one.
The agent symbolic learning framework introduces a unique approach to training language agents that is inspired by the learning process of neural networks. The framework maps agent components to neural network elements, enabling a backpropagation-like process. The main components are:
- Agent Pipeline: Represents a sequence of nodes that process input data.
- Node: An individual step in a pipeline, similar to a neural network layer.
- Trajectory: Stores information during the forward pass of gradient backpropagation.
- Language loss: Measuring the discrepancy between expected and actual outcomes in text.
- Linguistic gradients: Text analysis for updating agent components.
The training procedure includes a forward pass, linguistic loss calculation, backpropagation of linguistic gradients, and gradient-based updates using symbolic optimizers. These optimizers include PromptOptimizer, ToolOptimizer, and PipelineOptimizer, each designed to update a specific component of the agent system. The framework also supports batch training for more robust optimization.
The agent symbolic learning framework performs well across LLM benchmarks, software development, and creative writing tasks. It consistently outperforms other methods, with significant improvements seen on complex benchmarks such as MATH. In software development and creative writing, the framework's performance gap widens even further, outperforming specialized algorithms and frameworks. Its success is attributed to comprehensive optimization of the entire agent system, effectively finding optimal pipelines and prompts at each step. The framework demonstrates robustness and effectiveness in optimizing language agents for complex real-world tasks that are challenging with traditional methods, highlighting its potential to advance language agent research and applications.
The Agent Symbolic Learning Framework introduces an innovative approach to language agent optimization. Inspired by connectionist learning, we use language-based losses, gradients, and optimizers to jointly optimize all symbolic components in the agent system. This enables the agents to effectively handle complex real-world tasks and self-evolve after deployment. Experiments demonstrate superiority over a range of task complexities. By moving from model-centric to data-centric agent research, the framework represents a major step towards artificial general intelligence. Open-sourcing the code and prompts has the potential to accelerate progress in the field and revolutionize the development and application of language agents.
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Asjad is an Intern Consultant at Marktechpost. He is pursuing a B.Tech in Mechanical Engineering from Indian Institute of Technology Kharagpur. Asjad is an avid advocate of Machine Learning and Deep Learning and is constantly exploring the application of Machine Learning in Healthcare.
