Which statement is true about starting from an instruction-tuned model for function calling?

• A. It minimizes the additional training required for instruction-following and function calling.
• B. It increases the need for additional training for instruction-following.
• C. It prevents any training entirely.
• D. It has no impact on training requirements.

Answer: (A)

Starting from an instruction-tuned model helps because instruction tuning teaches the model to follow a wide range of human prompts, not just generate generic text. When you add function-calling capabilities, the model needs to know when and how to invoke tools and how to format those calls. A model already conditioned to follow explicit instructions is closer to the desired behavior, so you can achieve effective instruction-following and tool use with less extra training data and iterations.

This approach doesn’t remove the need for training entirely—there’s still work to align tool interfaces, ensure robust and safe tool use, and tailor the model to specific tasks. It also isn’t true that starting from an instruction-tuned model would increase training needs; in practice, it reduces the amount of additional tuning required compared to starting from a non-instruction-tuned base.