📝 SummarXiv

Abstract

Large Language Models (LLMs) have progressed beyond simple text creation, and tool use has become increasingly important for complex, real-world tasks. Tool use in LLMs refers to their ability to utilize external resources such as APIs, databases, or software functions to extend their functionality beyond generating text.Tools are used for tasks such as performing calculations, making API calls to retrieve the current time and date, and more. This capability enables models to fetch real-time data, execute commands, or solve problems requiring dynamic interaction, making it indispensable for applications like AI agents in virtual assistants, robotic control, or automated workflows. However, while LLMs are usually adept tool use, their vast resource requirements and computation complexity restrict their use in every use case.As a result, there is an increasing need for more compact and efficient Small Language Models (SLMs). Small language models (SLMs) struggle in tool use compared to large language models (LLMs). As soon in Table 1. SLMs are typically trained on smaller, more specific datasets, resulting in a narrower knowledge base and limited contextual understanding compared to LLMs. This research addresses these challenges by using Reinforcement Learning (RL), specifically Group Relative Policy Optimization (GRPO), to enhance tool-use proficiency in SLMs. Unlike conventional fine-tuning approaches that require heavy computation and often lack adaptability, our method provides an efficient, effective solution that significantly boosts SLM tool-use accuracy, increasing their practical utility.