Adapt a model without updating every weight

LoRA represents a weight update through low-rank trainable factors while keeping the pretrained weights fixed. QLoRA combines adapter training with a frozen quantized base model. These approaches change how the update is represented and stored; they do not, by themselves, determine which behavior the training data rewards.

An adapter can learn demonstrations or participate in a preference-training pipeline. Name both the objective and the update method. Calling an experiment LoRA explains the parameterization, but leaves open the data, optimizer, loss, and evaluation that define the actual intervention.

Report trainable parameters, memory use, total training tokens, wall time, hardware, and the precision used for training and evaluation. If an adapter method makes more experiments affordable, distinguish the benefit of extra search from the quality of one controlled training run.

Evaluate the model in the same merged or unmerged configuration that will be served. Track formatting, task accuracy, instruction following, and regression tests against the exact base. An inexpensive update is useful when it delivers a measurable gain on held-out work without quietly weakening behaviors the application depends on.