Fine-tuning
Further training of an existing model on a smaller, focused set of examples to change how it behaves.
Fine-tuning means taking a model that has already been trained and training it a little more on a smaller, targeted collection of examples. It is much cheaper than the original training, and it changes the model’s behaviour rather than teaching it everything from scratch.
A useful comparison is a graduate who already has a broad education and then takes a short course for a specific job. A general model might be fine-tuned to write in a company’s style, to answer medical questions more carefully, or to follow instructions in a helpful way.
Fine-tuning is also a safety topic. Companies use it to make models refuse harmful requests. However, studies suggest that a small amount of further fine-tuning can weaken those protections, which matters when a model’s internal settings are released publicly for anyone to adjust. In a 2023 study, as few as ten examples were enough to undo much of one commercial model’s safety training. People disagree about how serious this is and about whether releasing open models is worth the trade-off. Fine-tuning is one tool among several, and it does not on its own guarantee good behaviour.