NoisyAgent Framework Enhances LLM Agent Robustness in Real-World Settings
Researchers propose NoisyAgent, a training framework that improves large language model (LLM) agent performance under real-world noise. The framework addresses two noise sources: user noise (ambiguity in instructions) and tool noise (unreliable tool outputs). By incorporating these imperfections during training, NoisyAgent enhances agent robustness, reducing performance degradation observed when agents move from curated benchmarks to stochastic environments. The approach marks a shift from idealized training to realistic interaction dynamics.
Key facts
- LLM agents degrade in real-world settings due to mismatch with idealized training.
- NoisyAgent explicitly incorporates environmental imperfections into agent learning.
- Two noise sources identified: user noise and tool noise.
- Framework aims to bridge gap between benchmark performance and real-world deployment.
Entities
—