Although w is technically not updated when y =ŷ, it is still considered a new vector.
t is incremented for every training instance, so t ← t + 1 is inserted after the line 5. c is updated by adding the partial vector ∂ as follows (to be inserted after the line 7): each dimension in c represents the accumulative penalty (or reward) for a particular feature and a label.