# 在噪声中学习行动：通过噪声环境增强智能体鲁棒性

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-26 08:00
- AIHOT 分数：64
- AIHOT 链接：https://aihot.virxact.com/items/cmpnurml500gtslv4k7mht1hm
- 原文链接：https://arxiv.org/abs/2605.27209

## AI 摘要

现有大语言模型智能体在理想化基准测试中表现良好，但在具有固有随机性和缺陷的真实环境中部署时，性能常会下降。研究提出了NoisyAgent训练框架，旨在缩小这一差距。该框架通过模拟真实场景中的“用户噪声”（交互的歧义性）和“工具噪声”（工具执行失败）两类噪声源来增强智能体。训练过程中，噪声被策略性地施加于部分训练轮次，并随着模型适应而逐步增加难度。实验表明，该方法在噪声和动态环境中持续提升了智能体的鲁棒性，且在理想化基准测试上也获得了性能增益，证明了建模交互缺陷对于弥合训练与现实部署差距的重要性。

## 正文

Recent advances in large language models (LLMs) have facilitated the widespread deployment of LLMs as interactive agents capable of reasoning, planning, and tool use. Despite strong performance on existing benchmarks, such agents often exhibit notable degradation when deployed in real-world settings, where environments are inherently stochastic and imperfect. We argue that this discrepancy arises from a fundamental mismatch between idealized training settings and real-world interaction dynamics, where current paradigms rely on carefully curated task instructions and stable, well-controlled environments. To address this gap, we propose NoisyAgent, an agentic training framework that explicitly incorporates environmental imperfections into the agent learning process. We identify two major sources of interaction noise in real-world scenarios: user noise, which captures ambiguity and variability in user interaction, and tool noise, which reflects failures and anomalies in tool execution. We introduce such perturbations into the training pipeline by modifying user interaction patterns and simulating tool execution results within the training environment. To stabilize training while encouraging agents to handle increasingly challenging imperfections, noise is applied to only a subset of rollouts and progressively increased in difficulty as the model adapts to the current noise level. Extensive experiments demonstrate that our approach consistently improves agent robustness under noisy and dynamic environments. Our analysis reveals that training under noise conditions also yields performance gains on idealized benchmarks, suggesting that controlled exposure to environmental noise promotes more generalizable reasoning and decision-making behaviors. Our findings highlight the importance of modeling interaction imperfections for bridging the gap between agent training and real-world deployment.