构建可扩展的计算机操作数据生成流水线，训练出性能领先的通用计算机代理。
Fara-1.5: Scalable Learning Environments for Computer Use Agents
- 用真实网站与仿真环境结合，搭配多模型求解器和用户模拟器生成高质量操作数据。
- 三个验证器分别评估任务正确性、效率与关键步骤遵循度，确保数据质量。
- 推出开源的3个规模模型（4B/9B/27B），在浏览器任务上刷新同尺寸模型最佳表现。
从人类示范中收集计算机使用数据成本高且耗时，亟需可扩展的生成策略。本文提出FaraGen1.5，一个由环境、求解器和验证器组成的模块化数据流水线。该系统结合真实网页与仿真环境，能模拟需认证或不可逆操作的场景；通过支持多模型的求解器框架（含GPT-5.4等前沿模型）及用户模拟器，实现多轮交互式数据生成；并采用三类互补验证器评估任务正确性、执行效率与关键点遵循度。基于此流水线数据，我们训练了基于Qwen3.5的Fara1.5系列计算机使用代理（CUAs），包含4B、9B、27B三种规模。采用监督微调（SFT）策略，迭代平衡覆盖广度、高价值任务与模型缺陷修复。各模型在浏览器任务基准上均达新SOTA：Fara1.5-9B在Online-Mind2Web上达63.4%，WebVoyager上达86.6%；Fara1.5-27B在Online-Mind2Web上达72.3%，媲美更大规模闭源系统。模型权重已开源，许可为MIT。
原文摘要 · Abstract (English)
Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This requires two key ingredients: environments in which agents can act and verifiers that can judge whether their demonstrations succeeded. We introduce FaraGen1.5, a scalable data pipeline for computer use agents composed of three modular components: environments, solvers, and verifiers. FaraGen1.5 uses both live websites and synthetic environments that faithfully simulate domains gated by authentication or that require irreversible actions. It employs a solver harness that can be powered by multiple models, including strong frontier models such as GPT-5.4, and also incorporates a user simulator to enable multi-turn rollouts. Finally, FaraGen1.5 scores the resulting trajectories with three complementary verifiers covering task correctness, efficiency, and critical-point adherence. Using data produced by this pipeline, we train Fara1.5, a family of native computer use agents (CUAs) at three scales built on Qwen3.5 (4B, 9B, and 27B). To train these models, we employ a supervised finetuning (SFT) recipe that carefully balances data from FaraGen1.5 for broad coverage, specific high-value tasks, and target model deficiencies in an iterative approach. Each model sets a new state of the art (SoTA) for its size class on browser-use benchmarks: Fara1.5-9B reaches 63.4% on Online-Mind2Web and 86.6% on WebVoyager, while Fara1.5-27B achieves 72.3% on Online-Mind2Web, which is competitive with much larger proprietary systems. We also release weights for the Fara1.5 models under MIT license, making SoTA computer use accessible for all beyond closed API-only systems.
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