DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data
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 s...
| Authors | Venus Team, Sunhao Dai, Yong Deng et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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- inclusionAI/DR-Venus-4B-RL-GGUFModel · cited by
- inclusionAI/DR-Venus-4B-SFT-GGUFModel · cited by
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