Scaling Agents via Continual Pre-training
Liangcai Su, Zhen Zhang, Guangyu Li, Zhuo Chen, Chenxi Wang, Maojia Song, Xinyu Wang, Kuan Li
Abstract
Large language models (LLMs) have evolved into agentic systems capable of autonomous tool use and multi-step reasoning for complex problem-solving. However, post-training approaches building upon general-purpose foundation models consistently underperform in agentic tasks, particularly in open-source implementations. We identify the root cause: the absence of robust agentic foundation models forces models during post-training to simultaneously learn diverse agentic behaviors while aligning them to expert demonstrations, thereby creating fundamental optimization tensions. To this end, we are the first to propose incorporating Agentic Continual Pre-training (Agentic CPT) into the deep research agents training pipeline to build powerful agentic foundational models. Based on this approach, we develop a deep research agent model named AgentFounder. We evaluate our AgentFounder-30B on 10 benchmarks and achieve state-of-the-art performance while retains strong tool-use ability, notably 39.9% on BrowseComp-en, 43.3% on BrowseComp-zh, and 31.5% Pass@1 on HLE.
BibTeX
@inproceedings{
su2026scaling,
title={Scaling Agents via Continual Pre-training},
author={Liangcai Su and Zhen Zhang and Guangyu Li and Zhuo Chen and Chenxi Wang and Maojia Song and Xinyu Wang and Kuan Li and Jialong Wu and Xuanzhong Chen and Zile Qiao and Zhongwang Zhang and Huifeng Yin and Shihao Cai and Runnan Fang and Zhengwei Tao and Wenbiao Yin and Rui Ye and Yong Jiang and Ningyu Zhang and Pengjun Xie and Fei Huang and Kai Ye and Kewei Tu and Chenxiong Qian and Jingren Zhou},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=Dru5mm9anE}
}