distillation

Synthetic data is how teams get training data when real data is scarce, and it has sharp failure modes. The signal is naming when it helps, how to hold the quality line, and why training on model output across generations narrows the distribution.

Distillation buys you most of a frontier model's quality at a fraction of the serving cost. The signal is naming the three LLM-specific variants and knowing which one works against a closed API. Here is the answer.

Distillation buys you a small fast model that keeps most of a big model's quality. The signal is explaining why soft targets transfer more than hard labels, and knowing when to distill versus quantize versus prune. Here is the model-compression answer.

Pruning removes redundant weights to shrink and sometimes speed up a model. The signal is the structured-versus-unstructured split and why only structured pruning reliably wins latency on commodity hardware.