AIBullisharXiv – CS AI · 15h ago6/10
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Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments
Researchers introduce NoisyAgent, a training framework that improves large language model agent robustness by deliberately exposing them to environmental imperfections during training. By simulating real-world interaction noise—including user ambiguity and tool failures—the approach bridges the gap between idealized benchmark performance and practical deployment reliability.