[lunixbochs]

If you can reproduce the original information based only on a state input, you have stored it in the state (in an encoded form or not). If your state is smaller than the original information, you have compressed it. If your reproduction is not faithful to the original, you have created lossy compression.

If the future input samples have a meaningful impact during loop playback, then it hasn't learned the correct behavior of the original loop pedal.

Note that the linked project appears to use a hidden size of 20. Twenty floats. With that much space we're very much back to "sure, you might theoretically be able to loop if the information fits in the hidden size".

Increasing the hidden size beyond 20 still won't solve learning the complex state machine behavior of an original loop pedal, which can loop variable length audio. You'd need to provide the pedal state to the network in addition to the audio, and probably train need to train it on a bunch of different loop lengths (>thousands?).

This would mostly be an academic pursuit, as it's extremely impractical compared to the other uses of the device.