We study the various routes through which privileged sensing influences learning in Scaffolder. We replace each privileged component with a non-privileged counterpart to assess component-wise contributions. We see that dropping components generally hurts performance, and that the contribution of each component is task dependent. For example, Blind Pick has difficult exploration, so removing privileged exploration ("No Scaff. Explore") hurts the most, and in RGB Cube, privileged representation learning is important for encoding high-dimensional images, so ("No Scaff. Repr.") hurts the most.
In all these cases, the combined Scaffolder benefits from cohesively integrating these many routes for privileged sensing to influence policy learning, and performs best.