Offline Training Framework Shows Promise for Large-Scale Research Agents

Global: New Offline Training Framework Challenges Need for Online RL in Research Agents

A study released on Jan. 26, 2026 by a team of seven researchers from multiple institutions presents a novel approach to building deep research agents without relying on costly online reinforcement learning (RL). The paper, titled “OffSeeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents,” argues that offline training can achieve comparable performance while eliminating extensive API usage.

Background and Motivation

Deep research agents have demonstrated the ability to tackle long‑horizon tasks, yet state‑of‑the‑art results typically depend on online RL, which incurs substantial financial overhead due to repeated calls to external services. The authors note that the high expense limits broader adoption and scalability of such systems.

DeepForge Suite and Dataset

To address data scarcity, the researchers introduced DeepForge, an open‑source task synthesis framework that automatically generates large‑scale research queries. Using DeepForge, they assembled a curated collection comprising 66,000 question‑answer pairs, 33,000 supervised fine‑tuning (SFT) trajectories, and 21,000 direct preference optimization (DPO) pairs. The dataset is intended to support effective offline training of research agents.

OffSeeker Model and Performance

Leveraging the DeepForge resources, the team trained OffSeeker, an 8‑billion‑parameter model developed entirely offline. Evaluation across six established benchmarks revealed that OffSeeker leads among agents of similar size and remains competitive with systems that contain up to 30 billion parameters trained via intensive online RL.

Implications for Future Research

The findings suggest that offline methodologies, when paired with high‑quality synthetic data, can reduce the economic barriers associated with deep research agent development. Critics caution that broader testing on diverse tasks is needed to confirm generalizability, but the authors emphasize the potential for more accessible research‑agent pipelines.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.