this post was submitted on 01 Oct 2023
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They actually do. Training an AI involves changing some values in the model in an attempt for it to better fit an optimization function. It takes many tries to find a set of values that perform better, but a single try to confirm it does.
Both sides require much more computing power than for a single hash, but the difficulty imbalance is still there, and verifiers could change "how much better fit" the next model needs to be, just like they do by changing difficulty requirements right now.
True. The next iteration doesn't need to be optimal, just an improvement in the loss function.
Not sure how they would decide when to stop.