Poster Sat, Apr 25, 2026 • 6:30 AM – 9:00 AM PDT Pavilion 3 P3-#1917

Scaling Agents via Continual Pre-training

Liangcai Su ⋅ Zhen Zhang ⋅ Guangyu Li ⋅ Zhuo Chen ⋅ Chenxi Wang ⋅ Maojia Song ⋅ Xinyu Wang ⋅ Kuan Li ⋅ Jialong Wu ⋅ Xuanzhong Chen ⋅ Zile Qiao ⋅ Zhongwang Zhang ⋅ Huifeng Yin ⋅ Shihao Cai ⋅ Runnan Fang ⋅ Zhengwei Tao ⋅ Wenbiao Yin ⋅ Rui Ye ⋅ Yong Jiang ⋅ Ningyu Zhang ⋅ Pengjun Xie ⋅ Fei Huang ⋅ Kai Ye ⋅ Kewei Tu ⋅ Chenxiong Qian ⋅ Jingren Zhou

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.