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arXiv · 2504.16839

SMART: Tuning a symbolic music generation system with an audio domain aesthetic reward

Nicolas Jonason·Luca Casini·Bob L. T. Sturm
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Abstract

Recent work has proposed training machine learning models to predict aesthetic ratings for music audio. Our work explores whether such models can be used to finetune a symbolic music generation system with reinforcement learning, and what effect this has on the system outputs. To test this, we use group relative policy optimization to finetune a piano MIDI model with Meta Audiobox Aesthetics ratings of audio-rendered outputs as the reward. We find that this optimization has effects on multiple low-level features of the generated outputs, and improves the average subjective ratings in a preliminary listening study with $14$ participants. We also find that over-optimization dramatically reduces diversity of model outputs.

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Nicolas Jonason, Luca Casini, Bob L. T. Sturm. 2025-04-23. SMART: Tuning a symbolic music generation system with an audio domain aesthetic reward. https://arxiv.org/abs/2504.16839

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