做小模型领域专精的团队终于有了高效方案——LearnWeak用强模型找弱模型短板，自动生成针对性数据，比盲目合成数据效果好太多，做CUA或自动化代理的开发者值得一试。
计算机使用代理（CUA）在多个领域取得进展，但为每个领域部署大型专家模型成本高昂。小模型虽更实用，但在特定领域表现不均。研究发现，简单合成大规模训练数据效果有限。为此，研究者提出LearnWeak框架，利用强参考代理识别小模型的弱点，自动生成针对性任务和训练数据，并引入错误感知的专精目标，区分规划与执行错误。在OSWorld基准上，LearnWeak在八个领域平均提升EvoCUA-8B和OpenCUA-7B分别11.6和11.1个百分点，优于现有方法。
Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents
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.