Read the paper. They train RLCR on existing big math problems. They subtract a brier score penalty from the correctness reward. No new confidence labels are needed.
I did, in the first days Jev came out, when people were bringing it up. Another assumption. Please review the HN commenting guidelines, the one which starts with "Please don't comment on whether someone read an article." is relevant here.
Nothing in that paper changes that ML algorithms are dependent on the training data. We can step back from Jev and algos to consider Bayes Theorem. If your sample is not representative of the population, your resulting statistics will be off. The same is true here. If the data you train a model like Jev with is not representative, the probabilities and confidences it outputs will not be representative.
What makes Jev interesting is that it works well out of the box across domains. What people who are well known in the field believe is that this is the result of Typesafe having a really good training data set. People are saying similar of MiMo-2.6 today.
"Did you read the article" doesn't apply to a link someone put in a comment. If you are going to be a hall monitor, at least do it properly. You are just acting in bad faith at this point.
The relevant data is the reasoning trace. Doesn't need user data. You can learn from people's detailed reasoning steps how confident they are, even outside your domain.
