Thanks for the answer. I have think of that and it sounds very error prone.
In order to train the existing parameter after the adjustment you have to keep the right parameter and prune the unnecessary one. I haven’t try yet, but considering the large training epoch and so the number of adjustment need to make, it’s very likely to keep the wrong parameter in the next train.
I personally think adjust model in real time is very agile and should be a demanding feature, so I wonder if there are any best practice or framework already address this?