Agentic Proposing: Enhancing Large Language Model Reasoning via Compositional Skill Synthesis

Abstract

Advancing complex reasoning in large language models relies on high-quality, verifiable datasets, yet human annotation remains cost-prohibitive and difficult to scale. Current synthesis paradigms often face a recurring trade-off: maintaining structural validity typically restricts problem complexity, while relaxing constraints to increase difficulty frequently leads to inconsistent or unsolvable instances. To address this, we propose Agentic Proposing, a framework that models problem synthesis as a goal-driven sequential decision process where a specialized agent dynamically selects and composes modular reasoning skills. Through an iterative workflow of internal reflection and tool-use, we develop the Agentic-Proposer-4B using Multi-Granularity Policy Optimization (MGPO) to generate high-precision, verifiable training trajectories across mathematics, coding, and science. Empirical results demonstrate that downstream solvers trained on agent-synthesized data significantly outperform leading baselines and exhibit robust cross-domain generalization. Notably, a 30B solver trained on only 11,000 synthesized trajectories achieves a state-of-the-art 91.6% accuracy on AIME25, rivaling frontier-scale proprietary models such as GPT-5 and proving that a small volume of high-quality synthetic signals can effectively substitute for massive human-curated datasets.

Repository Summary

Agentic Proposing treats data synthesis as a sequential decision process over composable reasoning skills and trains an Agentic-Proposer-4B with MGPO, and a 30B solver trained on only 11K synthesized trajectories reaches 91.6% on AIME 2025.

Bibliographic Data

Title

Agentic Proposing: Enhancing Large Language Model Reasoning via Compositional Skill Synthesis

Authors

Zhengbo Jiao, Shaobo Wang, Zifan Zhang, Xuan Ren, Wei Wang, Bing Zhao, Hu Wei, Linfeng Zhang

Publication date

2026/02/03

Identifier

arXiv:2602.03279

DOI

10.48550/arXiv.2602.03279

PDF size

1.8 MB