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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The authors introduce a novel RMHMC type method based on a semi-separable Hamiltonian for use in hierarchical models. The aim of the paper is to enable computationally efficient sampling from hierarchical models where there is strong correlation structure between the parameters and the hyperparameters. The authors give a clear introduction to hierarchical models and RMHMC before describing the proposed semi-separable integrator. The "trick" used in this paper is to define metric tensors independently over the parameters and hyperparameters, which allows both sets of parameters to be updated iteratively based on a single Hamiltonian that combines both quantities.