Small Reasoning Models are Instruction Followers in Function Calling

2026-08-23

AI Summary

研究通过引入Instruction-Followed Function Calling框架，使用小型模型在指令跟随上下文中提高大型语言模型的功能调用准确性，优于现有方法，并支持高效部署。

Abstract

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.

Problem

• function calling
• instruction-following
• large language models

Innovation

• Instruction-Followed Function Calling
• small reasoning models
• function calling
• edge-computing
• quantization