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.

翻译：我们考虑一个由人工学习者组成的群体，目标是在训练资源受限的条件下优化整体性能指标。该问题源于对人类教育系统中同伴学习的研究。在此背景下，我们研究了交互式人工学习者网络中的自然知识扩散过程。所谓“自然”，是指反映人类同伴学习的过程：学生的内部状态和学习过程大多不透明，主要自由度在于协调者（该协调者可能在学习者被分配至同伴小组前对其进行评估）对同伴学习小组的组建。除其他发现外，我们通过实验表明，此类过程确实能有效利用训练资源，并有助于设计出既具备泛化能力又不易过度拟合噪声标签的模块化神经模型。