Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments

Problem
This preprint addresses the gap in the robustness of interactive agents, particularly large language models (LLMs), when deployed in real-world environments characterized by stochasticity and imperfections. Current training paradigms rely on idealized settings with curated task instructions, leading to performance degradation in practical applications. The authors argue that the lack of exposure to real-world noise—specifically user and tool interaction noise—limits the generalizability of agent behaviors.

Method
The authors propose NoisyAgent, a novel training framework that integrates environmental noise into the agent learning process. The framework identifies two primary sources of noise: user noise, which captures variability in user interactions, and tool noise, which reflects execution failures of tools. The training pipeline is modified to incorporate these perturbations by altering user interaction patterns and simulating tool execution anomalies. Noise is introduced progressively, applied to a subset of rollouts, and increased in difficulty as the model adapts. This approach stabilizes training while enhancing the agent’s ability to handle real-world imperfections.

Results
Extensive experiments demonstrate that NoisyAgent significantly enhances agent robustness in noisy environments. The authors report improvements in performance metrics across various benchmarks, although specific headline numbers and baseline comparisons are not detailed in the abstract. Notably, training under noisy conditions also yields performance gains on idealized benchmarks, indicating that exposure to noise fosters more generalizable reasoning and decision-making capabilities. The results suggest that agents trained with NoisyAgent outperform traditional training methods in both noisy and controlled settings.

Limitations
The authors acknowledge that their approach may not fully capture all types of real-world noise and that the training process could be computationally intensive due to the need for simulating various noise conditions. Additionally, the paper does not discuss the scalability of the method to more complex environments or the potential for overfitting to specific noise patterns. The lack of detailed quantitative results in the abstract limits the ability to assess the magnitude of improvements relative to specific baselines.

Why it matters
This work has significant implications for the development of more robust AI agents capable of functioning effectively in real-world scenarios. By modeling interaction imperfections, the NoisyAgent framework bridges the gap between training and deployment, potentially leading to more reliable and adaptable AI systems. The findings encourage further exploration of noise-influenced training paradigms, which could enhance the performance of LLMs and other interactive agents in dynamic environments.

Authors: Yuxin Chen, Xiaodong Cai, Junfeng Fang, Zhuowen Han, Yu Wang, Yaorui Shi, Yi Zhang, Qi Gu et al.
Source: arXiv:2605.27209
URL: https://arxiv.org/abs/2605.27209v1