  We consider a setting where a population of artificial learners is given, and
the objective is to optimize aggregate measures of performance, under
constraints on training resources. The problem is motivated by the study of
peer learning in human educational systems. In this context, we study natural
knowledge diffusion processes in networks of interacting artificial learners.
By `natural', we mean processes that reflect human peer learning where the
students' internal state and learning process is mostly opaque, and the main
degree of freedom lies in the formation of peer learning groups by a
coordinator who can potentially evaluate the learners before assigning them to
peer groups. Among else, we empirically show that such processes indeed make
effective use of the training resources, and enable the design of modular
neural models that have the capacity to generalize without being prone to
overfitting noisy labels.
