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.

在给定人工学习者的情景下，通过优化性能的综合指标并在培训资源有限的情况下进行约束，研究人际教育系统中的同伴学习。我们通过研究人工学习者之间相互作用网络中的自然知识传播过程来讨论问题。我们实证表明这类过程确实有效利用培训资源，并使得能够进行模块化的神经模型的设计，能够在不易受到噪声标签过度拟合的情况下进行泛化。