AgentFrontier: Expanding the Capability Frontier of LLM Agents with
  ZPD-Guided Data Synthesis
Authors: name: Xuanzhong Chen, name: Zile Qiao, name: Guoxin Chen, name: Liangcai Su, name: Zhen Zhang, name: Xinyu Wang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou, name: Yong Jiang
Organization: TongyiLab

Training large language model agents on tasks at the frontier of their
capabilities is key to unlocking advanced reasoning. We introduce a data
synthesis approach inspired by the educational theory of the Zone of Proximal
Development (ZPD), which defines this frontier as tasks an LLM cannot solve
alone but can master with guidance. To operationalize this, we present the
AgentFrontier Engine, an automated pipeline that synthesizes high-quality,
multidisciplinary data situated precisely within the LLM's ZPD. This engine
supports both continued pre-training with knowledge-intensive data and targeted
post-training on complex reasoning tasks. From the same framework, we derive
the ZPD Exam, a dynamic and automated benchmark designed to evaluate agent
capabilities on these frontier tasks. We train AgentFrontier-30B-A3B model on
our synthesized data, which achieves state-of-the-art results on demanding
benchmarks like Humanity's Last Exam, even surpassing some leading proprietary
agents. Our work demonstrates that a ZPD-guided approach to data synthesis
offers a scalable and effective path toward building more capable LLM agents.