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CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction
arXiv – CS AI|Yinghao Ma, Haiwen Xia, Hewei Gao, Weixiong Chen, Yuxin Ye, Yuchen Yang, Sungkyun Chang, Mingshuo Ding, Yizhi Li, Ruibin Yuan, Simon Dixon, Emmanouil Benetos||6 views
🤖AI Summary
Researchers introduce CMI-RewardBench, a comprehensive evaluation framework for music generation AI models that can process multimodal inputs including text, lyrics, and audio. The system includes a 110k sample preference dataset and reward models that show strong correlation with human judgments for music quality assessment.
Key Takeaways
- →CMI-RewardBench establishes the first unified benchmark for evaluating music reward models across multimodal inputs.
- →The framework includes CMI-Pref-Pseudo with 110k pseudo-labeled samples and human-annotated corpus for training.
- →CMI reward models demonstrate strong correlation with human judgments on musicality and alignment tasks.
- →The system enables inference-time scaling through top-k filtering for improved music generation.
- →All training data, benchmarks, and reward models are made publicly available for research use.
Read Original →via arXiv – CS AI
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