Starting from an instruction-tuned model for function calling yields which advantage?

• A. It increases training time to adapt to new tasks.
• B. It eliminates the need for any model customization.
• C. It reduces inference latency.
• D. It minimizes the additional training required for instruction-following and function calling.

Answer: (D)

Starting from an instruction-tuned model for function calling taps into the model’s existing ability to follow human instructions and know when to invoke external tools. Because it’s already been trained to understand and execute instructions, you need much less additional training to teach it instruction-following behavior and how to call functions. This means you can adapt to new tasks with only a small amount of task-specific data, or sometimes even with zero-shot setups, rather than starting from scratch with heavy fine-tuning. The other ideas don’t capture this efficiency: training time can actually be reduced, not increased; you still typically need some customization to align with new tool interfaces and safety constraints; and while latency is important, it isn’t the main benefit tied to starting from an instruction-tuned model for function calling.