We prove the almost-sure convergence of a class of sampling-based nested decomposition algorithms for multi-stage stochastic convex programs in which the stage costs are convex non necessarily linear functions of the decisions and the state, and uncertainty is modelled by a scenario tree. As a special case, our results imply the almost-sure convergence of SDDP, CUPPS and DOASA when applied to problems with general convex cost functions.