Grounding Computer Use Agents on Human Demonstrations
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2025
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| author | Feizi, Aarash Nayak, Shravan Jian, Xiangru Lin, Kevin Qinghong Li, Kaixin Awal, Rabiul Lù, Xing Han Obando-Ceron, Johan Rodriguez, Juan A. Chapados, Nicolas Vazquez, David Romero-Soriano, Adriana Rabbany, Reihaneh Taslakian, Perouz Pal, Christopher Gella, Spandana Rajeswar, Sai |
| author_facet | Feizi, Aarash Nayak, Shravan Jian, Xiangru Lin, Kevin Qinghong Li, Kaixin Awal, Rabiul Lù, Xing Han Obando-Ceron, Johan Rodriguez, Juan A. Chapados, Nicolas Vazquez, David Romero-Soriano, Adriana Rabbany, Reihaneh Taslakian, Perouz Pal, Christopher Gella, Spandana Rajeswar, Sai |
| contents | Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements. While large datasets exist for web and mobile interactions, high-quality resources for desktop environments are limited. To address this gap, we introduce GroundCUA, a large-scale desktop grounding dataset built from expert human demonstrations. It covers 87 applications across 12 categories and includes 56K screenshots, with every on-screen element carefully annotated for a total of over 3.56M human-verified annotations. From these demonstrations, we generate diverse instructions that capture a wide range of real-world tasks, providing high-quality data for model training. Using GroundCUA, we develop the GroundNext family of models that map instructions to their target UI elements. At both 3B and 7B scales, GroundNext achieves state-of-the-art results across five benchmarks using supervised fine-tuning, while requiring less than one-tenth the training data of prior work. Reinforcement learning post-training further improves performance, and when evaluated in an agentic setting on the OSWorld benchmark using o3 as planner, GroundNext attains comparable or superior results to models trained with substantially more data,. These results demonstrate the critical role of high-quality, expert-driven datasets in advancing general-purpose computer-use agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07332 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Grounding Computer Use Agents on Human Demonstrations Feizi, Aarash Nayak, Shravan Jian, Xiangru Lin, Kevin Qinghong Li, Kaixin Awal, Rabiul Lù, Xing Han Obando-Ceron, Johan Rodriguez, Juan A. Chapados, Nicolas Vazquez, David Romero-Soriano, Adriana Rabbany, Reihaneh Taslakian, Perouz Pal, Christopher Gella, Spandana Rajeswar, Sai Machine Learning Artificial Intelligence Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements. While large datasets exist for web and mobile interactions, high-quality resources for desktop environments are limited. To address this gap, we introduce GroundCUA, a large-scale desktop grounding dataset built from expert human demonstrations. It covers 87 applications across 12 categories and includes 56K screenshots, with every on-screen element carefully annotated for a total of over 3.56M human-verified annotations. From these demonstrations, we generate diverse instructions that capture a wide range of real-world tasks, providing high-quality data for model training. Using GroundCUA, we develop the GroundNext family of models that map instructions to their target UI elements. At both 3B and 7B scales, GroundNext achieves state-of-the-art results across five benchmarks using supervised fine-tuning, while requiring less than one-tenth the training data of prior work. Reinforcement learning post-training further improves performance, and when evaluated in an agentic setting on the OSWorld benchmark using o3 as planner, GroundNext attains comparable or superior results to models trained with substantially more data,. These results demonstrate the critical role of high-quality, expert-driven datasets in advancing general-purpose computer-use agents. |
| title | Grounding Computer Use Agents on Human Demonstrations |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.07332 |