Trial and error is pretty much what training is. You feed an input in and use the error to update the network. What is surprising with these models is that the

Trial and error is pretty much what training is. You feed an input in and use the error to update the network. What is surprising with these models is that the simple training leads to emergent behaviour that is much more powerful than what you’d expect from the training data. With RHLF post training you can tweak these emergent behaviours by having a human (or a model trained to act like a human) give feedback on how good the output is. So far I’ve not seen any good explanations for how this emergent behaviour happens or how it can be reverse engineered.