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 are out of your depth and grasping at straws.