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[2601.18467] OffSeeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents
Summary
The paper introduces OffSeeker, a model demonstrating that offline training can effectively replace costly online reinforcement learning for deep research agents, showcasing competitive performance with fewer resources.
Why It Matters
This research addresses the high costs associated with online reinforcement learning, offering a viable alternative through offline training. By providing a comprehensive suite for task synthesis and a large dataset, it enhances accessibility for researchers and developers in AI, potentially accelerating advancements in the field.
Key Takeaways
- Online reinforcement learning is expensive and resource-intensive.
- OffSeeker utilizes offline training to generate effective research agents.
- The study provides a task synthesis framework and a large dataset for training.
- OffSeeker outperforms similar-sized models and competes with larger systems.
- This approach could democratize access to advanced AI research capabilities.
Computer Science > Artificial Intelligence arXiv:2601.18467 (cs) [Submitted on 26 Jan 2026 (v1), last revised 22 Feb 2026 (this version, v2)] Title:OffSeeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents Authors:Yuhang Zhou, Kai Zheng, Qiguang Chen, Mengkang Hu, Qingfeng Sun, Can Xu, Jingjing Chen View a PDF of the paper titled OffSeeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents, by Yuhang Zhou and 6 other authors View PDF Abstract:Deep research agents have shown remarkable potential in handling long-horizon tasks. However, state-of-the-art performance typically relies on online reinforcement learning (RL), which is financially expensive due to extensive API calls. While offline training offers a more efficient alternative, its progress is hindered by the scarcity of high-quality research trajectories. In this paper, we demonstrate that expensive online reinforcement learning is not all you need to build powerful research agents. To bridge this gap, we introduce a fully open-source suite designed for effective offline training. Our core contributions include DeepForge, a ready-to-use task synthesis framework that generates large-scale research queries without heavy preprocessing; and a curated collection of 66k QA pairs, 33k SFT trajectories, and 21k DPO pairs. Leveraging these resources, we train OffSeeker (8B), a model developed entirely offline. Extensive evaluations across six benchmarks show that OffSeeke...
