Motor cortex (M1) exhibits a rich repertoire of activities to support the generation of complex movements. Recent network models capture many qualitative aspects of M1 dynamics, but they can generate only a few distinct movements (all of the same duration). We demonstrate that simple modulation of neuronal input–output gains in recurrent neuronal network models with fixed connectivity can dramatically reorganize neuronal activity and consequently downstream muscle outputs. We show that a relatively small number of modulatory control units provide sufficient flexibility to adjust high-dimensional network activity using a simple reward-based learning rule. Furthermore, novel movements can be assembled from previously-learned primitives and we can separately change movement speed while preserving movement shape. Our results provide a new perspective on the role of modulatory systems in controlling recurrent cortical activity.