Enterprise customer-support agents increasingly rely on Large Language Models (LLMs) to execute bounded, tool-using workflows. We study this problem through the lens of Skill LLMs: specialized executors that operate within a selected skill context and must complete a narrow, stateful workflow under policy and tool cons...
Md Tahmid Rahman Laskar, Gundeep Singh, Xue-Yong Fu et al.· 0 citations
This work studies whether task-specialist models should be handled by separate task-specialist models or by a single model trained through multi-task training, sequential updates, or model merging, and concludes with practical guidelines for selecting fine-tuning strategies in real-world settings.
It is found that SFT consistently improves text-turn success, and that overall next-turn success increases for every model under gold-history evaluation, but these improvements do not transfer to autonomous workflow execution.
Limited controlled evidence exists on how training data, adaptation method, and model scale jointly affect tool-calling performance in language-model agents. We evaluate supervised fine-tuning (SFT) with LoRA, reinforcement learning (RL) via Group Relative Policy Optimization (GRPO), and SFT followed by GRPO across six...
