BalanceSFT: Improving LLM Function Calling with Balanced Training Signals and Data Hardness

While Supervised Fine-Tuning (SFT) is the pre-vailing 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 Hard-ness , 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 Bal-anceSFT, 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.