DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914497651802112 |
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| author | Venus Team Dai, Sunhao Deng, Yong Lin, Jinzhen Song, Yusheng Wang, Guoqing Wu, Xiaofeng Zhou, Yuqi Yang, Shuo Ying, Zhenzhe Zhang, Zhanwei Meng, Changhua Wang, Weiqiang |
| author_facet | Venus Team Dai, Sunhao Deng, Yong Lin, Jinzhen Song, Yusheng Wang, Guoqing Wu, Xiaofeng Zhou, Yuqi Yang, Shuo Ying, Zhenzhe Zhang, Zhanwei Meng, Changhua Wang, Weiqiang |
| contents | Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and privacy. In this work, we study how to train a strong small deep research agent under limited open-data by improving both data quality and data utilization. We present DR-Venus, a frontier 4B deep research agent for edge-scale deployment, built entirely on open data. Our training recipe consists of two stages. In the first stage, we use agentic supervised fine-tuning (SFT) to establish basic agentic capability, combining strict data cleaning with resampling of long-horizon trajectories to improve data quality and utilization. In the second stage, we apply agentic reinforcement learning (RL) to further improve execution reliability on long-horizon deep research tasks. To make RL effective for small agents in this setting, we build on IGPO and design turn-level rewards based on information gain and format-aware regularization, thereby enhancing supervision density and turn-level credit assignment. Built entirely on roughly 10K open-data, DR-Venus-4B significantly outperforms prior agentic models under 9B parameters on multiple deep research benchmarks, while also narrowing the gap to much larger 30B-class systems. Our further analysis shows that 4B agents already possess surprisingly strong performance potential, highlighting both the deployment promise of small models and the value of test-time scaling in this setting. We release our models, code, and key recipes to support reproducible research on edge-scale deep research agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19859 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data Venus Team Dai, Sunhao Deng, Yong Lin, Jinzhen Song, Yusheng Wang, Guoqing Wu, Xiaofeng Zhou, Yuqi Yang, Shuo Ying, Zhenzhe Zhang, Zhanwei Meng, Changhua Wang, Weiqiang Machine Learning Artificial Intelligence Computation and Language Information Retrieval Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and privacy. In this work, we study how to train a strong small deep research agent under limited open-data by improving both data quality and data utilization. We present DR-Venus, a frontier 4B deep research agent for edge-scale deployment, built entirely on open data. Our training recipe consists of two stages. In the first stage, we use agentic supervised fine-tuning (SFT) to establish basic agentic capability, combining strict data cleaning with resampling of long-horizon trajectories to improve data quality and utilization. In the second stage, we apply agentic reinforcement learning (RL) to further improve execution reliability on long-horizon deep research tasks. To make RL effective for small agents in this setting, we build on IGPO and design turn-level rewards based on information gain and format-aware regularization, thereby enhancing supervision density and turn-level credit assignment. Built entirely on roughly 10K open-data, DR-Venus-4B significantly outperforms prior agentic models under 9B parameters on multiple deep research benchmarks, while also narrowing the gap to much larger 30B-class systems. Our further analysis shows that 4B agents already possess surprisingly strong performance potential, highlighting both the deployment promise of small models and the value of test-time scaling in this setting. We release our models, code, and key recipes to support reproducible research on edge-scale deep research agents. |
| title | DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data |
| topic | Machine Learning Artificial Intelligence Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2604.19859 |