🤗 Dataset   |   🤗 Model   |    📑 Paper    ｜   📖 Github

While Supervised Fine-Tuning (SFT) is the prevailing method for equipping Large Language Models (LLMs) with function calling capabilities, its effectiveness is often compromised by two critical challenges: 1) Imbalanced Training Signals, where lengthy Chain-of-Thought (CoT) reasoning tokens dominate the training signals over concise function calls in the learning objective, and 2) Imbalanced Data Hardness, characterized by a scarcity of hard training examples. To overcome these limitations, we propose Balanced Supervised Fine-tuning (BalanceSFT), a novel framework incorporates two key components: a Self-adjusted Signal Balancing (SSB) loss that employs a learnable hyperparameter to dynamically adjust the token contributions of CoT reasoning and function calls, together with a Hard Data Re-sampling (HDR) strategy that establishes a feedback loop to selectively generate new, high-quality complex data guided by model errors. Extensive experiments demonstrate the effectiveness of our proposed BalanceSFT framework. With BalanceSFT, a 7B model achieves function calling performance on par with state-of-the-art giants like GPT-4o. Our code, models, and dataset are open-sourced.

Overview of BalanceSFT's data refinement pipeline. It starts with a standard function call dataset, which is refined through a Base Quality Check and Answer Check to create initial training data and identify hard data. The model is first initialized via a Cold Start using the Self-adjusted Signal Balancing (SSB) Loss. Subsequently, the Hard Data Re-sampling (HDR) strategy creates a Self-evolving Loop where the model iteratively reasons on hard cases, generates new solutions, and undergoes quality-gated retraining.

We have released all the data that refined in this process, whcih contains high quality CoT data for function call.

@article{FunReason,
  title={FunReason: Enhancing Large Language Models' Function Calling via Self-Refinement Multiscale Loss and Automated Data Refinement},
  author={Bingguang Hao, Maolin Wang, Zengzhuang Xu, Cunyin Peng, Yicheng Chen, Xiangyu Zhao, Jinjie Gu, Chenyi Zhuang},
  journal={arXiv preprint arXiv:2505.20192},
  year={2025}
}