Ruppert [Handbook of Sequential Analysis, B. K. Ghosh and P. K. Sen, eds., Marcel Dekker, New York, 1991] and Polyak [Automat. Remote Control, 51 (1990), pp. 937-946] independently introduced the averaging principle for Robbins-Monro's algorithm in order to construct an algorithm that is asymptotically efficient, and that is quickly updated. Their averaging principle has been extended to other algorithms (including Kiefer-Wolfowitz's algorithm), leading to algorithms that are asymptotically efficient, but whose updatings are less straightforward. We introduce the use of two-time-scale stochastic approximation algorithms to construct a large class of asymptotically efficient algorithms. This method generalizes the averaging principle. Moreover, for the algorithms that do not behave as Robbins-Monro's algorithm, it allows one to provide algorithms whose updating is as straightforward as that of the averaged Robbins-Monro's algorithm.