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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.27209 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | V5IuAXV3R7N0 |
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| Publisher visit | Yes — open original |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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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