FunReason-MT Technical Report: Overcoming the Complexity Barrier in
  Multi-Turn Function Calling
Authors: name: Zengzhuang Xu; name: Bingguang Hao; name: Zechuan Wang; name: Yuntao Wen; name: Maolin Wang; name: Yang Liu; name: Long Chen; name: Dong Wang; name: Yicheng Chen; name: Cunyin Peng; name: Chenyi Zhuang; name: Jinjie Gu; name: Leilei Gan; name: Xiangyu Zhao; name: Shi Gu
Published: 2025-10-28 17:15:26

Abstract: Function calling (FC) empowers large language models (LLMs) and autonomous
agents to interface with external tools, a critical capability for solving
complex, real-world problems. As this ability becomes increasingly central to
advanced AI systems, the need for high-quality, multi-turn training data to
develop and refine it cannot be overstated. Existing data synthesis methods,
such as random environment sampling or multi-agent role-playing, are not
powerful enough to generate high-quality data in real-world environments.
Practical challenges come in three folds: targeted model training, isolation of
tool architecture, and multi-turn logical dependency. To address these
structural deficiencies, we present FunReason-MT, a novel data synthesis
framework for real-world multi-turn tool use. FunReason-MT resolves the
complexity barrier in multi-turn FC data by employing 1) Environment-API Graph
Interactions to gather varied high-quality trajectories, 2) Advanced Tool-Query
Synthesis to simplify hard query construction, and 3) Guided Iterative Chain
for sophisticated CoT generation. Evaluations on Berkeley Function-Calling
Leaderboard (BFCLv3) demonstrate the power of our framework: a 4B model built
upon FunReason-MT generated data achieves state-of-the-art performance among
comparable-sized models, outperforming most close-source models. Further
performance improvements on BFCLv4 confirm that FunReason-MT provides a
reliable and robust source for agentic learning.