Reviews: Tree-Structured Reinforcement Learning for Sequential Object Localization

I liked the ideas present in the paper. This is the first sequential search strategy I have seen that tries to output all objects in an image, and does not impose any arbitrary and hard-to-justify order on the boxes during training. I have a minor clarification, and then some ways of strengthening the experimental section, which to me would be the difference between a poster and an oral. A point of clarification: It seems that the ranking of the proposals output is simply the depth of the tree at which they are discovered. Why is this a good ranking?