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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...
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