Adaptation of the motor system to sensorimotor perturbations is a type of learning relevant for tool use and coping with an ever-changing body. Memory for motor adaptation can take the form of savings: an increase in the apparent rate constant of re-adaptation compared to that of initial adaptation. The assessment of savings is simplified if the sensory errors a subject experiences at the beginning of initial adaptation and the beginning of re-adaptation are the same. This can be accomplished by introducing either 1) a sufficiently small number of counter-perturbation trials (counter-perturbation paradigm, CP) or 2) a sufficiently large number of zero-perturbation trials (washout paradigm, WO) between initial adaptation and re-adaptation. A 2-rate, linear time-invariant state space model (SSMLTI,2) was recently shown to theoretically produce savings for CP. However, we reasoned from superposition that this model would be unable to explain savings for WO. Using the same task (planar reaching) and type of perturbation (visuomotor rotation), we found comparable savings for both CP and WO paradigms. While SSMLTI,2 explained some degree of savings for CP it failed completely for WO. We conclude that for visuomotor rotation, savings in general is not simply a consequence of LTI dynamics. Instead savings for visuomotor rotation involves meta-learning, which we show can be modeled as changes in system parameters across the phases of an adaptation experiment.