@ms_aifrontiers: Fara1.5 is here! The tech report just landed on arXiv. New SOTA for computer use agents of its size, and it competes wi…
Summary
Fara1.5 is a family of native computer use agents trained using the FaraGen1.5 scalable data pipeline. The models achieve new state-of-the-art results on browser-use benchmarks, competing with much larger frontier models.
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Fara1.5 is here!
The tech report just landed on arXiv. New SOTA for computer use agents of its size, and it competes with much larger frontier models.
Paper: https://t.co/BkhgwNuxiq
Fara-1.5: Scalable Learning Environments for Computer Use Agents
Source: https://arxiv.org/abs/2606.20785 Authors: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
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 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.
Submission history
From: Aravind Rajeswaran [view email] **[v1]**Thu, 18 Jun 2026 17:53:03 UTC (12,657 KB)
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