AgenticQwen trains small tool-use models with dual data flywheels

Yuanjie Lyu, Chengyu Wang, and Haonan Zheng introduce AgenticQwen, a family of small agentic language models designed for real-world tool use under strict cost and latency constraints. The models are trained using a framework that combines reasoning RL and agentic RL with two complementary data flywheels: a reasoning flywheel that increases task difficulty by learning from errors, and an agentic flywheel that expands linear workflows into multi-branch behavior trees. Validated on public benchmarks and an industrial agent system, AgenticQwen closes the performance gap with much larger models on search and data analysis tasks. Model checkpoints, synthetic data, and training code are publicly released.