🔥 CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents

💡 The paper addresses the problem of training computer-use agents using reinforcement learning with verifiable rewards, which is limited by the scarcity of scalable training data with deterministic rewards. To solve this, the authors propose CUA-Gym, a scalable pipeline that generates task instructions, environment states, and reward functions. The pipeline consists of a generator agent, a discriminator agent, and an orchestrator agent that work together to create high-quality training data. The generated data is then filtered using a combination of large language model majority voting and agent rollouts to ensure quality.

To further address the scarcity of training environments, the authors create CUA-Gym-Hub, a suite of high-fidelity mock web applications that mimic real-world software-use distributions. Using this pipeline, the authors construct a dataset of 32,112 verified training tuples grounded in 110 environments. They then train two models, CUA-Gym-A3B and CUA-Gym-A17B, using the dataset and achieve state-of-the-art performance on the OSWorld-Verified benchmark, with scores of 62.1% and 72.6% respectively.

The results demonstrate that the proposed pipeline and dataset can be used to train computer-use agents that outperform prior models at comparable scales. Additionally, the models show transferability beyond the training environments, as they also improve on the held-out WebArena benchmark. The authors plan to open-source the full synthesis pipeline, dataset, environments, and models, making it possible for others to build upon their work and further advance the field of computer-use agents. Overall, the paper presents a significant contribution to the field of reinforcement learning and computer-use agents, providing a scalable and effective way to train agents that can perform complex tasks in a variety of environments.

📅 Published on May 25

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2605.25624
• PDF: https://arxiv.org/pdf/2605.25624
• Project Page: https://cua-gym.xlang.ai

📊 Datasets citing this paper:
• https://huggingface.co/datasets/xlangai/CUA-Gym

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📢 By: https://xn--r1a.website/PaperNexus

#ComputerUseAgents #VerifiableRewards #ReinforcementLearning #TaskInstructionGeneration #ScalableTrainingEnvironments

🔥 Fara-7B: An Efficient Agentic Model for Computer Use

💡 The paper introduces FaraGen, a synthetic data generation system for computer use agents, which addresses the lack of large and high-quality datasets for training efficient models. The absence of such datasets has limited the progress of computer use agents, unlike large language models that have benefited from abundant textual data. FaraGen generates diverse tasks from frequently used websites, produces multiple solution attempts, and filters successful trajectories using multiple verifiers, achieving high throughput, yield, and diversity for multi-step web tasks at a low cost.

Using the data generated by FaraGen, the authors train Fara-7B, a native computer use agent model that perceives the computer using only screenshots and executes actions via predicted coordinates. Fara-7B is small enough to run on-device, making it efficient for practical applications. The model is evaluated on several benchmarks, including WebVoyager, Online-Mind2Web, and the newly introduced WebTailBench, which better captures under-represented web tasks.

The results show that Fara-7B outperforms other computer use agent models of comparable size on these benchmarks. Moreover, Fara-7B is competitive with much larger models, demonstrating the benefits of scalable data generation systems in advancing small and efficient agentic models. The authors are making Fara-7B available as open-source, along with the WebTailBench benchmark, to facilitate further research and development in the field of computer use agents. Overall, the paper contributes to the advancement of efficient and high-performing computer use agents by introducing a novel data generation system and a state-of-the-art model that can be used for a wide range of web tasks.

📅 Published on Nov 24, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2511.19663
• PDF: https://arxiv.org/pdf/2511.19663
• Project Page: https://aka.ms/msaif/fara

🤖 Models citing this paper:
• https://huggingface.co/microsoft/Fara-7B
• https://huggingface.co/AlexKitipov/Fara-7B
• https://huggingface.co/XythicK/microsoft_Fara-7B-GGUF

📊 Datasets citing this paper:
• https://huggingface.co/datasets/microsoft/WebTailBench
• https://huggingface.co/datasets/Archi-001/WebTailBench

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/2025-ai-timeline/2025-ai-timeline
• https://huggingface.co/spaces/prithivMLmods/CUA-GUI-Operator
• https://huggingface.co/spaces/HyperCluster/Fara-BrowserUse

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📢 By: https://xn--r1a.website/PaperNexus

#ComputerUseAgents #SyntheticDataGeneration #AgenticModels #WebTaskAutomation #EfficientModelTraining