Progress in computer use agents (CUAs) has been constrained by the absence of large and high-quality
\ndatasets that capture how humans interact with a computer. While LLMs have thrived on abundant
\ntextual data, no comparable corpus exists for CUA trajectories. To address these gaps, we introduce
\nFaraGen, a novel synthetic data generation system for multi-step web tasks. FaraGen can propose
\ndiverse tasks from frequently used websites, generate multiple solution attempts, and filter successful
\ntrajectories using multiple verifiers. It achieves high throughput, yield, and diversity for multi-step
\nweb tasks, producing verified trajectories at approximately $1 each. We use this data to train Fara-7B, a
\nnative CUA model that perceives the computer using only screenshots, executes actions via predicted
\ncoordinates, and is small enough to run on-device. We find that Fara-7B outperforms other CUA models
\nof comparable size on benchmarks like WebVoyager, Online-Mind2Web, and WebTailBench\u2013 our novel
\nbenchmark that better captures under-represented web tasks in pre-existing benchmarks. Furthermore,
\nFara-7B is competitive with much larger frontier models, illustrating key benefits of scalable data
\ngeneration systems in advancing small efficient agentic models. We are making Fara-7B open-weight on
\nMicrosoft Foundry and HuggingFace, and we are releasing WebTailBench.<\/p>\n","protected":false},"excerpt":{"rendered":"
Progress in computer use agents (CUAs) has been constrained by the absence of large and high-quality datasets that capture how humans interact with a computer. While LLMs have thrived on abundant textual data, no comparable corpus exists for CUA trajectories. To address these gaps, we introduce FaraGen, a novel synthetic data generation system for multi-step 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