Fine-tuning

Additional training starting from an existing model: usually smaller data and narrower objective than pretraining.

Fine-tuning adapts a pretrained model e.g. foundation LLM weights toward a domain, tone, tool-use policy, or classifier head with specialized labeled pairs.

Compared with training from scratch, it is usually cheaper when a strong base model already exists: though bias in fine-tune data can still compound.