Eliezer made that point nicely with respect to LLMs here:
Consider that somewhere on the internet is probably a list of thruples: <product of 2 prime numbers, first prime, second prime>.
GPT obviously isn’t going to predict that successfully for significantly-sized primes, but it illustrates the basic point:
There is no law saying that a predictor only needs to be as intelligent as the generator, in order to predict the generator’s next token.
Indeed, in general, you’ve got to be more intelligent to predict particular X, than to generate realistic X. GPTs are being trained to a much harder task than GANs.
Same spirit: <Hash, plaintext> pairs, which you can’t predict without cracking the hash algorithm, but which you could far more easily generate typical instances of if you were trying to pass a GAN’s discriminator about it (assuming a discriminator that had learned to compute hash functions).
Eliezer made that point nicely with respect to LLMs here: