Practical Solutions for Language Agent Optimization
Challenges in Language Agent Development
Developing language agents faces challenges due to the manual decomposition of tasks and limited adaptability. Researchers are seeking a transition to a more data-centric learning paradigm.
Introducing Agent Symbolic Learning Framework
AIWaves Inc. introduces a new approach for training language agents inspired by neural network learning. This framework enables comprehensive optimization of all symbolic components, avoiding local optima and supporting multi-agent systems.
Key Components of the Framework
- Agent Pipeline: Represents the sequence of nodes processing input data.
- Nodes: Individual steps within the pipeline, similar to neural network layers.
- Trajectory: Stores information during the forward pass for gradient back-propagation.
- Language Loss: Textual measure of discrepancy between expected and actual outcomes.
- Language Gradient: Textual analyses for updating the agent components.
Performance and Applications
The agent symbolic learning framework demonstrates superior performance across LLM benchmarks, software development, and creative writing tasks. It consistently outperforms other methods, showing significant improvements on complex benchmarks like MATH.
Advancing Language Agent Research and Applications
This framework represents a significant step towards artificial general intelligence by shifting from model-centric to data-centric agent research. The open-sourcing of code and prompts aims to accelerate progress in this field, potentially revolutionizing language agent development and applications.
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