You can’t learn to simulate an undo of a hash, or at least I have no idea what you are “simulating” and why that would be “easier”. You are certainly not simulating the generation of the hash, going token by token forwards you don’t have access to a pre-image at that point.
Of course the reason why sometimes hashes are followed by their pre-image in the training set is because they were generated in the opposite order and then simply pasted in hash->pre-image order.
I’ve seen LLMs generating text backwards. Theoretically, LLM can keep pre-image in activations, calculate hash and then output in order hash, pre-image.
You can’t learn to simulate an undo of a hash, or at least I have no idea what you are “simulating” and why that would be “easier”. You are certainly not simulating the generation of the hash, going token by token forwards you don’t have access to a pre-image at that point.
Of course the reason why sometimes hashes are followed by their pre-image in the training set is because they were generated in the opposite order and then simply pasted in hash->pre-image order.
I’ve seen LLMs generating text backwards. Theoretically, LLM can keep pre-image in activations, calculate hash and then output in order hash, pre-image.