✨Behavior Knowledge Merge in Reinforced Agentic Models

📝 Summary:
Reinforced Agent Merging RAM improves integrating RL agents by distinguishing shared and task-specific parameters. This preserves critical behaviors, outperforming baselines and unlocking synergistic performance beyond specialized agents.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13572
• PDF: https://arxiv.org/pdf/2601.13572
• Project Page: https://xiangchi-yuan.github.io/ram-project/
• Github: https://github.com/xiangchi-yuan/mrl

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✓ https://xn--r1a.website/DataScienceT

#ReinforcementLearning #MultiAgentSystems #ArtificialIntelligence #DeepLearning #AgenticModels

🔥 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

🔥 Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent

💡 The paper introduces Agents-A1, a 35 billion parameter Mixture-of-Experts Agentic Model that achieves performance comparable to trillion-parameter models by scaling the agent horizon instead of the parameters. The problem addressed is how to improve the performance of large language models on long-horizon tasks without increasing the number of parameters. The method used is a three-stage training approach, which includes supervised fine-tuning, domain-level teacher models, and multi-teacher distillation. The model is trained on a long-horizon knowledge-action infrastructure that connects external knowledge, actions, observations, and verifier outcomes, producing agentic trajectories with an average length of 45,000 tokens. The results show that Agents-A1 achieves strong and broad performance on long-horizon agent benchmarks, outperforming or matching the results of 1 trillion parameter models on several tasks, including SEAL-0, IFBench, HiPhO, FrontierScience-Olympiad, and MolBench-Bind. The paper provides a practical path for scaling the horizon using a smaller model that can reach or match the performance of larger models on long-horizon tasks.

📅 Published on Jun 29

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.30616
• PDF: https://arxiv.org/pdf/2606.30616
• Project Page: https://internscience.github.io/Agents-A1/

🤖 Models citing this paper:
• https://huggingface.co/InternScience/Agents-A1
• https://huggingface.co/InternScience/Agents-A1-FP8-dynamic
• https://huggingface.co/Abiray/Agents-A1-Q4_K_M-GGUF

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

#MixtureOfExperts #AgenticModels #LongHorizonTasks #LargeLanguageModels #ParameterEfficientTraining