You can update a model with new data at any time. Production models are often updated at intervals with monitoring. There are a few challenges to updating continuously.
Distributed models would have to be updated. How do we update weights from two sources? (There might be options for this, I haven't looked.)
Potential for undesirable and unstable predictions/generations.
I think you'd have to allow the weights to update pretty dramatically at each inference to get any real variation. I think this would lead to #2
Attention components probably do what you're looking for more accurately and efficiently.
CabSauce t1_j8nuthg wrote
Reply to [D] Is anyone working on ML models that infer and train at the same time? by Cogwheel
You can update a model with new data at any time. Production models are often updated at intervals with monitoring. There are a few challenges to updating continuously.