Figure 2 shows this property of exp-loss in contrast to log-loss in a binary classification problem.
as in adaBoost, a distribution over observations is defined:    Update % 10: until stopping criteria as it can be seen from Line 4 in algorithm 2, the feature that was added to the ensemble at each round is determined by a function of the gradient of the sequential exp-loss function (Eq.