Multi-Objective Instruction Selection is an instructional optimization method that simultaneously addresses multiple conflicting objectives—such as model performance, computational efficiency, and data diversity—during the selection of training instances, distinguishing it from single-objective approaches by its explicit balancing of coexisting goals in the learning process.