instruction tuning

Instruction tuning is what turns a raw next-token predictor into something that follows instructions. The signal is placing it correctly in the pretrain to SFT to alignment pipeline and knowing exactly what it does and does not fix.

Fine-tuning is mostly a data problem, not a hyperparameter one. The signal is naming what makes a good set (quality, diversity, format, dedup) and arguing why a few thousand clean examples beat a million noisy ones.

A few thousand carefully chosen examples can beat a million scraped ones. The signal is knowing which filters matter, how you measure example quality, and why diversity beats raw volume.