# 从小弱点学习：面向小型计算机使用智能体的自动化领域特化

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-27 08:00
- AIHOT 分数：63
- AIHOT 链接：https://aihot.virxact.com/items/cmpoxnk880a1zslv4ax5e4ldf
- 原文链接：https://arxiv.org/abs/2605.28775

## AI 摘要

针对小型开放计算机使用智能体在特定领域能力较弱且失败案例分布不均的问题，研究团队提出了LearnWeak框架。该框架利用一个更强的参考智能体，自动识别学生智能体在目标领域的弱点，并据此合成针对性任务以构建训练数据。LearnWeak进一步引入错误感知特化目标，能够区分规划与执行错误，实现更精确的行为更新。在OSWorld基准测试中，该方法使EvoCUA-8B和OpenCUA-7B模型在八个领域的平均性能分别提升了11.6和11.1个百分点。

## 正文

Computer-use agents (CUAs) have recently made substantial progress, but deploying a separate large expert for each software domain remains expensive. Small open computer-use agents are more practical specialization targets, but they remain substantially weaker and exhibit uneven domain-specific failures. A straightforward remedy is to synthesize large-scale training data for the target domain, yet we find that this naive approach yields only marginal improvements. Building on this observation, we introduce LearnWeak, an annotation-free specialization framework for small computer-use agents that uses a stronger reference agent to identify the student's weaknesses in the target domain, synthesize targeted tasks, and construct supervision automatically. LearnWeak further introduces an error-aware specialization objective that disentangles planning and execution errors, enabling more behaviorally precise updates than broad uniform supervision. On OSWorld, LearnWeak achieves average gains of 11.6 and 11.1 percentage points over EvoCUA-8B and OpenCUA-7B, respectively, across eight domains. We also validate that our student-aware dataset generation and training approaches outperform existing autonomous trajectory generation and training baselines. Our work highlights the importance of student awareness in both data synthesis and agent training, pointing toward a more principled and efficient path for specializing small computer-use agents in diverse domains.