Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments
The paper discusses the development of NoisyAgent, a training framework aimed at enhancing the robustness of agents in noisy environments. It identifies two main sources of interaction noise: user noise and tool noise, which are incorporated into the training process. The findings suggest that training under such conditions not only improves performance in real-world scenarios but also enhances generalizability on idealized benchmarks.
- ▪NoisyAgent incorporates environmental imperfections into the agent learning process.
- ▪The framework addresses user noise and tool noise to improve agent robustness.
- ▪Experiments show that training under noise conditions leads to better performance in both real-world and idealized settings.
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Computer Science > Artificial Intelligence arXiv:2605.27209 (cs) [Submitted on 26 May 2026] Title:Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments Authors:Yuxin Chen, Xiaodong Cai, Junfeng Fang, Zhuowen Han, Yu Wang, Yaorui Shi, Yi Zhang, Qi Gu, Xunliang Cai, Xiang Wang, An Zhang, Tat-Seng Chua View a PDF of the paper titled Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments, by Yuxin Chen and 11 other authors View PDF HTML (experimental) Abstract:Recent advances in large language models (LLMs) have facilitated the widespread deployment of LLMs as interactive agents capable of reasoning, planning, and tool use.
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