📄 论文总结
生成式音乐AI与人类偏好的对齐：方法与挑战 / Aligning Generative Music AI with Human Preferences: Methods and Challenges
1️⃣ 一句话总结
这篇论文探讨了如何通过偏好对齐技术，让生成式音乐AI更好地理解并满足人类对音乐和谐性、连贯性和主观质量的复杂偏好，以推动其在互动创作和个性化服务中的应用。
🤖 系统
Abstract - 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.