  Autoregressive generative transformers are key in music generation, producing
coherent compositions but facing challenges in human-machine collaboration. We
propose RefinPaint, an iterative technique that improves the sampling process.
It does this by identifying the weaker music elements using a feedback model,
which then informs the choices for resampling by an inpainting model. This
dual-focus methodology not only facilitates the machine's ability to improve
its automatic inpainting generation through repeated cycles but also offers a
valuable tool for humans seeking to refine their compositions with automatic
proofreading. Experimental results suggest RefinPaint's effectiveness in
inpainting and proofreading tasks, demonstrating its value for refining music
created by both machines and humans. This approach not only facilitates
creativity but also aids amateur composers in improving their work.
