Objective. Body-machine interfaces (BoMIs) translate human body movements into commands for controlling external devices, such as computer cursors. This process allows researchers to study the development and refinement of inverse models, which generate motor commands necessary for achieving desired movements. Traditionally, motor learning has focused on solo practice, but recent research has shifted towards exploring dyadic tasks, where two individuals practice together. Within dyadic tasks, synergic practice—where partners collaborate toward a shared goal—has shown promise in enhancing performance and reducing stress. However, the impact of contributions of each partner within synergic practice on individual and collective learning remains underexplored. This study aims to (i) investigate how different levels of contribution during synergic practice affect both individual and collective motor learning, and (ii) assess the impact of these contribution levels on individual performance when returning to solo practice. Approach. Forty naïve participants underwent individual practice, dyadic synergic practice, and a final round of individual practice using a BoMI to control a cursor. Participants were classified as high or low contributors based on their participation in the cursor trajectory during dyadic practice. We analyzed how these contribution levels influenced performance, motor strategies, and internal models during and after the dyadic phase. Main results. During dyadic practice, high contributors maintained motor strategies similar to their initial solo performance, while low contributors showed significant deviations. After returning to solo practice, high contributors retained better task performance, whereas low contributors initially regressed but quickly improved with additional practice, eventually matching high contributors’ performance levels. Significance. This understanding holds practical implications for optimizing dyadic practice. Our study sheds light on the influence of synergic practice on subsequent individual motor performance, contributing to a clearer understanding of its advantages and limitations for optimal implementation.