  Continual Learning (CL) has generated attention as a method of avoiding
Catastrophic Forgetting (CF) in the sequential training of neural networks,
improving network efficiency and adaptability to different tasks. Additionally,
CL serves as an ideal setting for studying network behavior and Forward
Knowledge Transfer (FKT) between tasks. Pruning methods for CL train
subnetworks to handle the sequential tasks which allows us to take a structured
approach to investigating FKT. Sharing prior subnetworks' weights leverages
past knowledge for the current task through FKT. Understanding which weights to
share is important as sharing all weights can yield sub-optimal accuracy. This
paper investigates how different sharing decisions affect the FKT between
tasks. Through this lens we demonstrate how task complexity and similarity
influence the optimal weight sharing decisions, giving insights into the
relationships between tasks and helping inform decision making in similar CL
methods. We implement three sequential datasets designed to emphasize variation
in task complexity and similarity, reporting results for both ResNet-18 and
VGG-16. By sharing in accordance with the decisions supported by our findings,
we show that we can improve task accuracy compared to other sharing decisions.
