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.
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.
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.
you are still employing underhanded techniques in an attempt "win an internet debate" (my impression)
try being more accommodating and flexible over repeating the same lame things
it's not hard to say, "ah I see what you were trying to say..." and move towards a more constructive conversation
RLCR / Jev et al. can only give as accurate predictions and probabilities as the underlying data they are trained on represents. Biased data results in biased probabilities, no algorithm fixes this. Can we agree on this point?
https://www.youtube.com/watch?v=c1Fv1uKTd-w
oh-seven