# 'Propagation' in n-step Sarsa

I am trying to understand the algorithm for n-step Sarsa from Sutton/Barto (2nd Edition, p. 157, PDF) As I understand it, this algorithm should update n state action values, but I cannot see where it is 'propagated backwards' (sorry for the wrong terminology, but I couldn't find something better). Probably, I am not seeing the forrest for all the trees?

The important part, where you can see a single reward value is used for $$n$$ different updates, is the part where a sum of $$R_i$$ values with $$i$$ ranging from $$\tau + 1$$ to $$\tau + n$$ is assigned to $$G$$.
So yes, the outer loop of the algorithm always does at most one update per iteration, but for that update it uses multiple previously observed $$R_i$$ values. Each of those $$R_i$$ values is used for multiple updates (not multiple updates at the same time, but multiple updates spread out over different iterations).