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.
We were talking about Jev and probability, now you're changing the problem, a rhetorical trick some people try to employ.
Another that uses dice rolling, coin flips, and an inventory level example to drive home the point that Jev's output are not real probabilities for outcomes.
I taught it (ML course; a day on RL, at a university), you should really stop making assumptions friend. Data quality and coverage matters in learning algorithms.
My initial comment and every one following is about RLCR and that paper. You don't appear to grasp the basics of that paper, it's reward function or how the optimizer is updating weights.
> You are out of your depth and grasping at straws.
Do you have any credentials or evidence that others can use to determine if this statement is not more accurately describing the author who wrote it?
Perhaps a PhD in ML, research output like published papers, or teaching/professional experience - all things I have
We could debate the merits of the paper contents, but I suspect you have intentionally moved on to personal attacks. Regardless, nothing you have said (nor can be found in this paper) has been a counter argument that learning algorithms are sensitive to training data, where the measured output difference is used by the optimization algorithm when updating the parameters. Garbage in, garbage out is a saying for a reason. No algorithm fixes non-representative data.
>you have to have training data with accurate probabilities
This was your claim. If you can't read and understand that paper in relation to your claim, you are out of your depth. You haven't made a single claim relevant to that paper - just hand wavy comments about data.
We both know who is
> just acting in bad faith at this point.