This talk presents intelligent auto-tuners that emulate expert decision-making in program design to automatically generate high-performance sparse tensor programs. By modeling the program construction process as a sequence of expert actions, the auto-tuner leverages techniques inspired by behavior cloning from imitation learning. Instead of exhaustively searching the design space, it directly predicts a sequence of actions to synthesize optimized programs, significantly improving search efficiency in complex, high-dimensional spaces.