Fuzzy rule interpolation (FRI) offers a useful means for reducing the complexity of fuzzy models and more importantly, it makes inference possible in sparse rule-based systems. An interpolative reasoning system may encounter a large number of interpolated rules during the process of performing FRI, which are commonly discarded once the outcomes of the input observations are obtained. However, these rules may contain potentially useful information, e.g., covering regions that were uncovered by the original sparse rule base. Thus, such rules should be exploited in order to improve the overall system coverage and efficacy. This paper presents an initial attempt towards a dynamic fuzzy rule interpolation framework, for the purpose of selecting, combining, and promoting informative, frequently used intermediate rules into the rule base. Simulations are employed to demonstrate the proposed method, showing better accuracy and robustness than that achievable through conventional FRI that uses just the original sparse rule base.