Fara-1.5: Scalable Learning Environments for Computer Use Agents

Ahmed Awadallah; Sahil Gupta; Yash Lara; Yadong Lu; Hussein Mozannar; Akshay Nambi; Zach Nussbaum; Yash Pandya; Aravind Rajeswaran; Corby Rosset; Alexey Taymanov; Luiz do Valle; Vibhav Vineet; Spencer Whitehead; Andrew Zhao

arXiv · 2606.20785

Fara-1.5: Scalable Learning Environments for Computer Use Agents

Ahmed Awadallah·Sahil Gupta·Yash Lara·Yadong Lu·Hussein Mozannar·Akshay Nambi·Zach Nussbaum·Yash Pandya·Aravind Rajeswaran·Corby Rosset·Alexey Taymanov·Luiz do Valle·Vibhav Vineet·Spencer Whitehead·Andrew Zhao

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Abstract

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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Ahmed Awadallah, Sahil Gupta, Yash Lara, Yadong Lu, Hussein Mozannar, Akshay Nambi, Zach Nussbaum, Yash Pandya, Aravind Rajeswaran, Corby Rosset, Alexey Taymanov, Luiz do Valle, Vibhav Vineet, Spencer Whitehead, Andrew Zhao. 2026-07-22. Fara-1.5: Scalable Learning Environments for Computer Use Agents. https://arxiv.org/abs/2606.20785

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