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DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent
arXiv – CS AI|Tongzhou Wu, Yuhao Wang, Xinyu Ma, Xiuqiang He, Shuaiqiang Wang, Dawei Yin, Xiangyu Zhao||2 views
🤖AI Summary
Researchers have released DeepResearch-9K, a large-scale dataset with 9,000 questions across three difficulty levels designed to train and benchmark AI research agents. The accompanying open-source framework DeepResearch-R1 supports multi-turn web interactions and reinforcement learning approaches for developing more sophisticated AI research capabilities.
Key Takeaways
- →DeepResearch-9K provides 9,000 challenging questions with verified answers to address the lack of real-world difficulty datasets for AI research agents.
- →The dataset includes high-quality search trajectories and reasoning chains from Tongyi-DeepResearch-30B-A3B, a state-of-the-art deep-research agent.
- →DeepResearch-R1 is an open-source training framework supporting multi-turn web interactions and various reinforcement learning approaches.
- →Agents trained on this dataset achieved state-of-the-art results on challenging deep-research benchmarks.
- →Both the dataset and training framework are publicly available on Hugging Face and GitHub respectively.
#ai-research#deep-research#datasets#machine-learning#reinforcement-learning#open-source#benchmarks#question-answering#web-agents
Read Original →via arXiv – CS AI
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