>WRONG! ENJOY MORE PENALTY, SCRUB! Is that true tho? During training, the model predicts {"wall": 0.65, "fence": 0.25, "river": 0.03}. Then backprop modifies t

>WRONG! ENJOY MORE PENALTY, SCRUB! Is that true tho? During training, the model predicts {"wall": 0.65, "fence": 0.25, "river": 0.03}. Then backprop modifies the weights such that it produces {"wall": 0.67, "fence": 0.24, "river": 0.02} next time. But it does that with a much richer feedback than WRONG! because we're also telling the model how much more likely "fence" is than "wall" in an indirect way. It's likely most of the neurons that supported "wall" also supported "fence", so the average neuron that supported "river" gets penalised much more than a neuron that supported "fence". I agree that distillation is more efficient for exactly the same reason, but I think even models as old as GPT-3 use this trick to work as well as they do.