Visuomotor adaptation involves explicit strategies and implicit recalibration, but their interaction remains unclear. Strategies can take two forms: algorithmic strategies, involving mental simulation of motor solutions, and retrieval strategies, which rely on previously successful solutions. These strategies arise from distinct neural circuits, which are likely to influence cerebellar-dependent implicit recalibration in different ways. To explore this, we created conditions favoring algorithmic (visuomotor mental rotation) or retrieval strategies by varying training target set size, as retrieval is limited by working memory. We controlled for generalization and intertrial effects, isolating implicit recalibration. Preparation times confirmed distinct strategy adoption. While the magnitudes of implicit recalibration were similar, generalization breadth was narrower with retrieval strategies, suggesting stricter stimulus-response associations. Algorithmic strategies produced broader generalization. These findings confirm that algorithmic and retrieval strategies impact implicit recalibration differently, and demand that future efforts to characterize the pattern of implicit generalization must account for the unique contribution of different forms of explicit strategies.