AgentFrontier: Expanding the Capability Frontier of LLM Agents with
  ZPD-Guided Data Synthesis
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.
69017da8646208eac0d1f419
Xuanzhong Chen
claimed_verified
2025-10-29T12:44:05.057Z
65e6970d135c27ea806526fe
/avatars/4aced113d9cab055ae06f3945869a280.svg
Xuanzhong Chen
chenxz
user
chenxz
69017da8646208eac0d1f41a
Zile Qiao
69017da8646208eac0d1f41b
Guoxin Chen
claimed_verified
2025-10-29T12:44:14.903Z
63f06116f1a47aaea5bd497b
/avatars/7d99ffa59c4579599e852a0ffb261268.svg
Guoxin Chen
GuoxinChen
user
GuoxinChen
69017da8646208eac0d1f41c
Liangcai Su
claimed_verified
2025-10-29T12:43:39.443Z
677f945ea82c316db164a180
/avatars/50ec99d971564944de3b1d9c17d50cfd.svg
Liangcai Su
HKU-Liangcai
user
HKU-Liangcai
69017da8646208eac0d1f41d
Zhen Zhang
69017da8646208eac0d1f41e
Xinyu Wang
claimed_verified
2025-10-30T14:42:20.614Z
64b73f9317570fdff9b0d1c4
/avatars/62124ae3e929b53f99f37e97226a877d.svg
Wang Xinyu
oriuta
user
oriuta
69017da8646208eac0d1f41f
Pengjun Xie
69017da8646208eac0d1f420
Fei Huang
69017da8646208eac0d1f421
Jingren Zhou
69017da8646208eac0d1f422
Yong Jiang
AgentFrontier: Expanding the Capability Frontier of LLM Agents with
  ZPD-Guided Data Synthesis
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.
A ZPD-guided data synthesis approach enhances large language model capabilities by training them on frontier tasks with automated guidance, achieving state-of-the-art results.
Zone of Proximal Development (ZPD) AgentFrontier Engine high-quality multidisciplinary data continued pre-training targeted post-training ZPD Exam dynamic benchmark complex reasoning tasks AgentFrontier-30B-A3B Humanity's Last Exam