Developmental compression is inspired by organismal development, in which an individual gradually changes over its life. Mutation in this context can affect either the base program or the developmental target toward which the base linearly develops during simulation (in Pyrosim). See paper (cite) for more details. This method exploits the mild impacts of developmental mutations to lessen adverse changes to previously-evolved capabilities and `compresses' specialized neural networks into a generalized one. In the absence of domain knowledge, developmental compression produces systems that avoid overt specialization, alleviating the need to engineer a bespoke system for every task permutation and suggesting better scalability than existing approaches.