Training computer-use agents (CUAs) -- models that interact with graphical desktops through screenshots and keyboard/mouse actions -- requires large-scale, diverse trajectory data collected in full desktop environments. The largest public resource, AgentNet (22.5K human trajectories), leads to negative transfer when used for supervised fine-tuning (SFT): continuing training UI-TARS 7B on AgentNet causes OSWorld success rate to fall from 26.3% to 8-10%. We present ProCUA-SFT, a dataset of 3.1M step-level SFT samples distilled from 93K synthetic trajectories across 2,484 application combinations. The dataset is produced by a fully automated pipeline that (i) synthesizes grounded tasks on live desktops seeded with real-world content -- 912 spreadsheets from SpreadsheetBench, approximately 10K permissively-licensed presentations from Zenodo10K, and multi-application OSWorld configs -- and (ii) verifies each task's feasibility through binary precondition checking before rollout. A single VLM (Kimi-K2.5) serves as goal generator, precondition judge, and trajectory executor, eliminating planner-actor capability gaps. Each trajectory is expanded into step-prefix samples that exactly reproduce the context layout seen at inference time. Fine-tuning UI-TARS 7B on ProCUA-SFT for one epoch yields 45.0% on OSWorld -- an 18.7 percentage-point improvement over the base model and over 35% above AgentNet-trained counterparts. A subset of ProCUA was incorporated into the training data for the Nemotron 3 Nano Omni model, contributing to its computer-use capabilities.

翻译：训练计算机使用智能体——即通过截屏和键盘/鼠标操作与图形桌面交互的模型——需要在完整桌面环境中收集大规模、多样化的轨迹数据。目前最大的公开资源AgentNet（含2.25万条人类轨迹）在用于监督微调时会导致负迁移：在UI-TARS 7B模型上继续训练AgentNet数据，将使其在OSWorld任务上的成功率从26.3%降至8-10%。本文提出ProCUA-SFT数据集，包含从涵盖2484种应用组合的9.3万条合成轨迹中蒸馏得到的310万步级SFT样本。该数据集通过全自动流水线生成，其流程包括：（i）在注入真实世界内容的活跃桌面上合成接地任务——这些内容包括来自SpreadsheetBench的912个电子表格、从Zenodo10K获取的约1万个开放许可演示文稿，以及多应用OSWorld配置；（ii）在轨迹执行前通过二元前置条件检查验证每项任务的可行性。单个视觉语言模型（Kimi-K2.5）同时担任目标生成器、前置条件判断器和轨迹执行器，从而消除了规划器与执行器之间的能力鸿沟。每条轨迹被扩展为步前缀样本，精确复现推理时的上下文布局。在ProCUA-SFT上对UI-TARS 7B模型进行单周期微调后，其在OSWorld上的成功率提升至45.0%：较基础模型提高18.7个百分点，较使用AgentNet训练的模型高出35%以上。ProCUA的子集已被纳入Nemotron 3 Nano Omni模型的训练数据，为其计算机使用能力的提升做出了贡献。