MuseCritic: Learning Multi-Aspect Song Rewards through Natural-Language Aesthetic Critiques
Abstract
Long-form song generation models continue to improve in duration, structural integrity, and acoustic complexity, making reliable aesthetic rewards increasingly important for aligning these models with human preferences. However, reward models for complete songs remain limited, and existing evaluators typically predict scores in a single forward pass without providing readable explanations. We introduce MUSECRITIC, a semi-scalar reward model that generates a natural-language critique covering five aesthetic dimensions and uses it as an intermediate representation to predict continuous reward scores. MUSECRITIC follows a two-stage training pipeline: a teacher model first provides high-quality critiques for supervised fine-tuning, after which the fine-tuned model generates its own critiques for reward learning, mitigating distribution shift between training and inference. On an in-domain test set of 200 SongEval songs, MUSECRITIC reduces macro-averaged mean squared error from 0.2875 to 0.2316 and improves macro-averaged LCC, SRCC, and Kendall's tau to 0.9068, 0.8838, and 0.7178, respectively. On the out-of-domain Music Arena benchmark with 733 preference pairs, it achieves the highest accuracy of 71.35%. Moreover, using MUSECRITIC with GRPO improves Muse-0.6B on all nine aesthetic metrics from SongEval and Audiobox Aesthetics. These results demonstrate that critique-conditioned reward modeling reduces scoring error and provides an effective optimization signal for song generation. The project repository is available at https://github.com/WuqnEl/MuseCritic.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
MuseCritic introduces a critique-conditioned reward modeling framework for long-form song generation, demonstrating that natural-language aesthetic critiques serve as effective intermediate representations for improving score prediction accuracy and enabling successful reinforcement learning alignment.
Methodology
The paper proposes MuseCritic, a semi-scalar reward model for long-form song generation that integrates natural-language aesthetic critiques as an intermediate representation before predicting continuous scores. The methodology involves a two-stage training pipeline: first, supervised fine-tuning (SFT) of an audio-language model backbone using critiques generated by a powerful external teacher (Gemini-3-Pro) conditioned on expert scores; second, reward learning where the model generates its own critiques (self-generated) to mitigate distribution shift, followed by training a reward head to predict scores conditioned on these self-generated critiques. This "critique-then-score" approach is conceptually sound and aligns with recent trends in LLM-as-a-Judge and reasoning-enhanced reward modeling. However, the novelty is somewhat limited by the reliance on a black-box teacher for data generation and the use of standard LoRA fine-tuning on a large foundation model, rather than proposing a fundamentally new architectural primitive for audio processing.
Results & evaluation
The experimental evaluation is comprehensive and rigorous. The authors evaluate on in-domain SongEval metrics (MSE, LCC, SRCC, KTAU) and out-of-domain Music Arena preference accuracy. MuseCritic consistently outperforms baselines, including a retrained SongEval (UTMOS) baseline and Gemini-3.1-Pro. Crucially, the ablation studies effectively isolate the contributions of the critique generation, self-generated critiques vs. offline critiques, and SFT initialization. The downstream reinforcement learning experiment using GRPO to optimize Muse-0.6B with MuseCritic as the reward model demonstrates practical utility, showing improvements across multiple aesthetic metrics. The use of deterministic decoding and fixed-prefix evaluation windows adds robustness to the downstream results.
Reproducibility
The paper provides detailed implementation details, including hyperparameters, training configurations, and data splitting strategies. The code repository is linked. The use of a specific external teacher (Gemini-3-Pro) for data generation is a potential reproducibility hurdle for others without access to that specific model version, but the prompt templates are provided. The data splitting is clearly defined.
Limitations
The primary limitation is the computational cost and latency introduced by the autoregressive critique generation step, which makes inference slower than direct score regression. Additionally, the method relies heavily on the quality of the external teacher for initial data generation, which may introduce biases or hallucinations if not properly verified (though the authors claim human verification). The evaluation is somewhat limited to Chinese and English vocal songs, and the generalizability to other genres or languages is not fully explored.
Broader impact
This work contributes to the field of AI-generated content evaluation and alignment. By providing a more interpretable and potentially more accurate reward model for song generation, it facilitates better optimization of generative models via reinforcement learning. This can lead to higher quality AI-generated music and more reliable evaluation benchmarks. The emphasis on interpretable critiques also aligns with the broader goal of making AI systems more transparent and trustworthy. MuseCritic introduces a critique-conditioned reward modeling framework for long-form song generation, demonstrating that natural-language aesthetic critiques serve as effective intermediate representations for improving score prediction accuracy and enabling successful reinforcement learning alignment.
Author of this paper? Send a correction.