Aligning Generative Music AI with Human Preferences: Methods and Challenges
Recent advances in generative AI for music have achieved remarkable fidelity and stylistic diversity, yet these systems often fail to align with nuanced human preferences due to the specific loss functions they use. This paper advocates for the systematic application of preference alignment techniques to music generation, addressing the fundamental gap between computational optimization and human musical appreciation. Drawing on recent breakthroughs including MusicRL's large-scale preference learning, multi-preference alignment frameworks like diffusion-based preference optimization in DiffRhythm+, and inference-time optimization techniques like Text2midi-InferAlign, we discuss how these techniques can address music's unique challenges: temporal coherence, harmonic consistency, and subjective quality assessment. We identify key research challenges including scalability to long-form compositions, reliability amongst others in preference modelling. Looking forward, we envision preference-aligned music generation enabling transformative applications in interactive composition tools and personalized music services. This work calls for sustained interdisciplinary research combining advances in machine learning, music-theory to create music AI systems that truly serve human creative and experiential needs.
691ebbb53c64d32b036459cf
Dorien Herremans
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2025-11-20T08:48:40.235Z
655431b2997379e9b0999d23
https://cdn-avatars.huggingface.co/v1/production/uploads/655431b2997379e9b0999d23/OhHCPCXfOS61Z7CSewUCU.png
Dorien Herremans
dorienh
user
dorienh
691ebbb53c64d32b036459d0
Abhinaba Roy
Aligning Generative Music AI with Human Preferences: Methods and Challenges
Recent advances in generative AI for music have achieved remarkable fidelity and stylistic diversity, yet these systems often fail to align with nuanced human preferences due to the specific loss functions they use. This paper advocates for the systematic application of preference alignment techniques to music generation, addressing the fundamental gap between computational optimization and human musical appreciation. Drawing on recent breakthroughs including MusicRL's large-scale preference learning, multi-preference alignment frameworks like diffusion-based preference optimization in DiffRhythm+, and inference-time optimization techniques like Text2midi-InferAlign, we discuss how these techniques can address music's unique challenges: temporal coherence, harmonic consistency, and subjective quality assessment. We identify key research challenges including scalability to long-form compositions, reliability amongst others in preference modelling. Looking forward, we envision preference-aligned music generation enabling transformative applications in interactive composition tools and personalized music services. This work calls for sustained interdisciplinary research combining advances in machine learning, music-theory to create music AI systems that truly serve human creative and experiential needs.
Preference alignment techniques, such as those in MusicRL and DiffRhythm+, are proposed to enhance music generation by addressing human preferences and unique challenges like temporal coherence and harmonic consistency.
MusicRL preference learning diffusion-based preference optimization DiffRhythm+ Text2midi-InferAlign temporal coherence harmonic consistency preference-aligned music generation interactive composition tools personalized music services