Exploration and exploitation are two cornerstones of evolutionary multiobjective optimization. Most of the existing works pay more attention to the exploitation, which mainly focuses on the fitness assignment and environmental selection. However, the exploration, usually realized by traditional genetic search operators, such as crossover and mutation, has not been fully addressed yet. In this paper, we propose a general learning paradigm based on Jumping Genes (JG) to enhance the exploration ability of multiobjective evolutionary algorithms. This paradigm adapts the JG to the continuous search space, and its activation is completely adaptive during the evolutionary process. Moreover, in order to efficiently utilize the useful information, only non-dominated solutions eliminated by the environmental selection are chosen for the secondary exploitation. Empirical studies demonstrate that the performance of a baseline algorithm can be significantly improved by the proposed paradigm.