Definition
Multi-task optimization tackles a family of related problems jointly rather than one at a time, so that structure discovered while solving one instance can transfer to the others. When the tasks share a common objective landscape — for example a Pareto frontier spanning several CUDA kernels — a strong solution found for one task can migrate to its neighbors, making the joint search far cheaper than running each in isolation.