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 web tasks. FaraGen can propose diverse tasks from frequently used websites, generate multiple solution attempts, and filter successful trajectories using multiple verifiers. It achieves high throughput, yield, and diversity for multi-step web tasks, producing verified trajectories at approximately $1 each. We use this data to train Fara-7B, a native CUA model that perceives the computer using only screenshots, executes actions via predicted coordinates, and is small enough to run on-device. We find that Fara-7B outperforms other CUA models of comparable size on benchmarks like WebVoyager, Online-Mind2Web, and WebTailBench -- our novel benchmark that better captures under-represented web tasks in pre-existing benchmarks. Furthermore, Fara-7B is competitive with much larger frontier models, illustrating key benefits of scalable data generation systems in advancing small efficient agentic models. We are making Fara-7B open-weight on Microsoft Foundry and HuggingFace, and we are releasing WebTailBench.

"It's a fight for talent…It's like'Game of Thrones' out there," Erik Caldwell, chief operating officer for supply chain in the Americas and Asia Pacific at XPO Logistics Inc., XPO 2.83% said at an industry conference earlier this year, discussing the company's use of robots to fulfill online orders. The use of robotics and other automation technology in industrial operations is growing, although the vast majority of warehouse work remains largely manual. About 16.5% of organizations across several industries including warehousing are now using commercial service robots, and 21.5% have them in pilot programs, according to a 2018 survey of 600 respondents by research firm IDC. The holiday shopping season highlights a warehouse-worker squeeze that is driving more logistics operators to embrace automation, as the growth of online commerce pushes more retail sales from storefronts to distribution centers. Online fulfillment centers--where companies like Amazon.com Inc. AMZN -0.94% pick, pack and ship consumer orders--require two to three times as many workers as traditional warehouses.