Internalizing Tool Knowledge in Small Language Models via QLoRA Fine-Tuning

ArXi:2605.17774v1 Announce Type: new Large language models are increasingly used as planning components in agentic systems, but current tool-use pipelines often require full tool schemas to be included in every prompt, creating substantial token overhead and limiting the practicality of smaller models. This paper investigates whether tool-use knowledge can be internalized into small language models through parameter-efficient fine-tuning, enabling structured planning without explicit tool descriptions at inference time.