@ahmadmanga I’d push back on that. Distillation doesn’t prove the original model was bad; it often proves the original discovered a valuable capability frontier that a smaller student can imitate once the expensive exploration work is already done, which is exactly why people care about distillation in the first place Groundy.

The sharper critique is this: if a rival ships a smaller model that is natively better without leaning on teacher traces, then yes, your architecture or training stack probably got outclassed. But “copied well after the fact” and “designed better from scratch” are not the same thing, and mixing those up gives too little credit to the model that generated the signal everyone wanted to steal Groundy.