FunReason-MT Technical Report: Overcoming the Complexity Barrier in
  Multi-Turn Function Calling
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.
6901786f646208eac0d1f399
Zengzhuang Xu
6901786f646208eac0d1f39a
Bingguang Hao
claimed_verified
2025-10-29T12:45:37.042Z
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Bingguang Hao
Bingguang
user
Bingguang
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Zechuan Wang
6901786f646208eac0d1f39c
Yuntao Wen
claimed_verified
2025-11-10T09:32:57.979Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/649ab4bdd4ae399f67296904/9czHlG4RwfkYW07sPP4IL.png
Yuntao Wen
skysss
user
skysss
6901786f646208eac0d1f39d
Maolin Wang
6901786f646208eac0d1f39e
Yang Liu
6901786f646208eac0d1f39f
Long Chen
6901786f646208eac0d1f3a0
Dong Wang
6901786f646208eac0d1f3a1
Yicheng Chen
6901786f646208eac0d1f3a2
Cunyin Peng
claimed_verified
2025-11-04T20:27:29.606Z
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Cunyin Peng
cherryPCY
user
cherryPCY
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Chenyi Zhuang
6901786f646208eac0d1f3a4
Jinjie Gu
6901786f646208eac0d1f3a5
Leilei Gan
6901786f646208eac0d1f3a6
Xiangyu Zhao
6901786f646208eac0d1f3a7
Shi Gu
FunReason-MT Technical Report: Overcoming the Complexity Barrier in
  Multi-Turn Function Calling
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.
FunReason-MT is a novel data synthesis framework that enhances multi-turn function calling in large language models by addressing targeted training, tool architecture isolation, and logical dependencies, achieving state-of-the-art performance on the Berkeley Function-Calling Leaderboard.
Function calling large language models autonomous agents multi-turn training data Environment-API Graph Interactions Advanced Tool-Query Synthesis Guided Iterative Chain Berkeley Function-Calling Leaderboard