🔥 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

🔥 GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation

💡 The paper introduces GraphGen, a framework designed to improve supervised fine-tuning for large language models by generating high-quality synthetic data. The problem addressed is that fine-tuning large language models requires substantial amounts of high-quality supervised data, which is costly and labor-intensive to acquire. Existing synthetic data generation approaches often suffer from factual inaccuracies, insufficient long-tail coverage, simplistic knowledge structures, and homogenized outputs.

To address these challenges, GraphGen constructs a fine-grained knowledge graph from the source text and identifies knowledge gaps in large language models using the expected calibration error metric. It prioritizes the generation of question-answering pairs that target high-value, long-tail knowledge. GraphGen also incorporates multi-hop neighborhood sampling to capture complex relational information and employs style-controlled generation to diversify the resulting question-answering data.

The framework is designed for three key question-answering scenarios: atomic question-answering, aggregated question-answering, and multi-hop question-answering. Experimental results on knowledge-intensive tasks under closed-book settings demonstrate that GraphGen outperforms conventional synthetic data methods, offering a more reliable and comprehensive solution to the data scarcity challenge in supervised fine-tuning. The code and data are publicly available, making it a valuable resource for the research community. Overall, GraphGen provides a novel approach to synthetic data generation, addressing the limitations of existing methods and improving the performance of large language models.

📅 Published on May 26, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2505.20416
• PDF: https://arxiv.org/pdf/2505.20416
• Project Page: https://huggingface.co/spaces/chenzihong/GraphGen

📊 Datasets citing this paper:
• https://huggingface.co/datasets/chenzihong/GraphGen-Data

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/chenzihong/GraphGen

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

#KnowledgeGraphConstruction #SyntheticDataGeneration #LargeLanguageModels #SupervisedFineTuning #LLMSupervision