The As-Rigid-As-Possible (ARAP) energy [SA07] has become a versatile ingredient in various geometry processing and machine learning methods. The classic method for its minimization is a block coordinate descent, alternating between local rotation estimation and a global linear solve, which converges slowly for large problem instances. We develop and evaluate a multi-level scheme targeted specifically at the optimization of the ARAP energy on large meshes. The main points of our approach are (1) a mesh hierarchy that provides the necessary control over topology while being fast, (2) methods for upsampling the rotations from coarser to finer levels of the hierarchy, and (3) using direct solvers for the linear system. The resulting optimization, remarkably, yields smaller energy while typically being faster on a large number of test cases. The hierarchical approach generalizes to related energies and compares favorably to acceleration schemes such as ADMM, which, in turn, also profit from the hierarchical approach.