I am trying to understand this too.

Supervised learning you train on pairs of (x, y) where x is your input (title/post text/metadata) and y is the output score.

Naively, it's a linear regression model, Y = b0 + b1x1 + b2x2 + b3x3. Where b0 is your bias ("a floor for score points"), and b1, b2, and b3 are bias terms for the actual data of the post. You can solve this, closed form, and find the b1/b2/b3 that minimize the error of fitting to Y.

How do these equations change with RL? I always assumed RL was a multi-step process where actions are taken to get to a reward. If there is only 1 step/decision, to produce a "random" score, it feels much like supervised learning.