When Another Training Round Stops Buying Learning
TL;DR for operators Closed-loop self-training should not be treated as productive merely because the system is still generating examples and updating weights. Wang et al. propose measuring whether each new round contains patterns that were not already predictable from the previous round, then using that signal to decide both which samples deserve gradient weight and whether another round is worth running at all.1 ...