DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data

TL;DR

DR-Venus is a small 4B parameter deep research agent trained solely on 10K open data, combining supervised fine-tuning and reinforcement learning to outperform larger models on research benchmarks.

Contribution

The paper introduces DR-Venus, a novel small-scale deep research agent built on open data, with a new training recipe combining data quality improvements and RL enhancements.

Findings

DR-Venus outperforms prior 9B models on multiple benchmarks.

It narrows the performance gap to larger 30B systems.

Small 4B agents show strong potential for edge deployment.

Abstract

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.