小模型通过遵循指令实现高效函数调用，适合边缘设备部署。
Small Reasoning Models are Instruction Followers in Function Calling
- 将函数调用逻辑交给小型专用模型，由其遵循指令执行
- 在推理型大模型上性能超越原生与提示调用基线，提升显著
- 支持强量化后仍保持高精度，适合移动端轻量部署
函数调用是智能体类大语言模型的核心能力。现有研究主要通过微调、强化学习和多智能体框架提升大模型的函数调用准确率，尤其针对原生函数调用的大模型。本文发现，大模型在标准用户-助手交互（即指令跟随场景）中比在工具调用场景下表现出更高的函数调用准确率。为此，我们提出一种新型框架——指令跟随函数调用（IFFC），将函数调用逻辑从主大模型中解耦，交由一个专用的小型模型在指令跟随范式下执行。该方法在多个基准上持续优于原生函数调用（NFC）和基于提示的函数调用（PFC）基线，尤其在推理导向型大模型上表现突出。此外，我们证明IFFC在激进量化条件下仍能保持稳健性能，支持无明显精度损失的高效本地化部署。本工作为边缘计算场景下可靠且资源高效的函数调用提供了新范式。
原文摘要 · Abstract (English)
Function calling represents the core capability of agentic large language models (LLMs). Existing research has focused on enhancing LLMs function-calling accuracy through fine-tuning, reinforcement learning (RL), and multi-agent frameworks, particularly for native function-calling LLMs. This work demonstrates that LLMs achieve superior accuracy in function calling in instruction-following contexts (i.e., standard user-assistant interactions) rather than a tool calling context. We introduce Instruction-Followed Function Calling (IFFC), a novel framework that decouples function-calling logic from the primary LLM and delegates it to a dedicated smaller model operating within the instruction-following paradigm. Our method consistently outperforms both native function calling (NFC) and prompt-based function calling (PFC) baselines, with particularly strong gains on reasoning-oriented LLMs. Furthermore, we demonstrate that IFFC maintains robust performance under aggressive quantization, enabling efficient on-device deployment without significant accuracy degradation. This work establishes a new paradigm for reliable, resource-efficient function calling in edge-computing scenarios.
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