Early Stopping

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 ...

Tokens are tiny invoices. One reasoning model writes a long chain-of-thought, checks itself, circles back, restates the same conclusion in a slightly more spiritual tone, and then finally prints an answer. Another model reaches the same answer halfway through but keeps talking because nobody told it that the meter is still running. This is not philosophy. This is unit economics with better typography. ...