@inproceedings{zhou-etal-2026-offseeker,
title = "{O}ff{S}eeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents",
author = "Zhou, Yuhang and
Zheng, Kai and
Chen, Qiguang and
Hu, Mengkang and
Sun, Qingfeng and
Xu, Can and
Chen, Jingjing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.173/",
pages = "3966--3986",
ISBN = "979-8-89176-459-0",
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, a model developed entirely offline. Extensive evaluations across six benchmarks show that OffSeeker not only leads among similar-sized agents but also remains competitive with 30B-parameter systems trained via heavy online RL."
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%0 Conference Proceedings
%T OffSeeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents
%A Zhou, Yuhang
%A Zheng, Kai
%A Chen, Qiguang
%A Hu, Mengkang
%A Sun, Qingfeng
%A Xu, Can
%A Chen, Jingjing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhou-etal-2026-offseeker
%X 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, a model developed entirely offline. Extensive evaluations across six benchmarks show that OffSeeker not only leads among similar-sized agents but also remains competitive with 30B-parameter systems trained via heavy online RL.
%U https://aclanthology.org/2026.emnlp-main.173/
%P 3966-3986
Markdown (Informal)
[OffSeeker: Online Reinforcement Learning Is Not All You Need for Deep Research Agents](https://aclanthology.org/2026.emnlp-main.173/) (Zhou et al., EMNLP 2026)
ACL