The consideration of technological learning in energy system models is of crucial importance as modeling future energy pathways across several sectors by considering cost developments influences model results significantly. Implementing experience curves in energy system models is one of few methods to consider the relation between cumulative installed capacity deployment and unit cost reductions of a technology. In this chapter the endogenous and exogenous approach of implementing technological learning in different energy system models are compared and analyzed in detail. Therefore the corresponding strengths and limitations of these approaches are encountered as well as possible solutions to overcome these constraints are estimated. To determine the influence of uncertainty in experience curves, sensitivity analyses with three different bottom-up models are conducted. The analysis of the literature and the lessons learned from the REFLEX project reveal that the endogenous approach is feasible, especially for top-down models but related to several challenges. Thus a balance between modeling accuracy and increasing complexity needs to be maintained while interpreting modeling results carefully.