LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training
Abstract
Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts. Existing language model-based systems face a structural trade-off: mixed-token modeling preserves vocal-instrument coordination but obscures track-specific details, whereas dual-track prediction improves acoustics but requires longer sequences and weakens global planning. We present LeVo 2, a hybrid LLM-Diffusion framework for controllable full-length song generation. LeVo 2 formulates this trade-off as hierarchical modeling: LeLM first predicts mixed tokens for semantic planning, then predicts vocal and accompaniment tokens in parallel for track-specific refinement, while a diffusion-based Music Codec reconstructs full-length waveforms. A central contribution of this extended version is an aesthetics-guided training schedule for alignment. During pre-training, an automated music aesthetic evaluation framework assigns musicality-tier conditions to large-scale data, providing musicality priors before preference alignment. Progressive post-training applies SFT, large-scale offline DPO, and closed-loop semi-online DPO to separately improve generation quality, controllability, and musicality. Modular extension then trains the Track-Specific LM for acoustic refinement while preserving the aligned semantic planner. This schedule separates musicality learning, controllability alignment, and acoustic refinement, mitigating optimization conflict and the limitations of static offline preference pairs. Expert listening tests and objective evaluations show that LeVo 2 outperforms open-source baselines across six subjective dimensions, and approaches leading commercial systems on several listening metrics. Ablations validate the effects of the training strategy, aesthetics guidance, scaling, and hierarchical architecture.
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
LeVo 2 introduces a hierarchical LLM-Diffusion framework with a decoupled, aesthetics-guided progressive post-training strategy that effectively balances global musical coherence with track-specific acoustic fidelity, achieving state-of-the-art performance among open-source models.
Methodology
The paper proposes LeVo 2, a hybrid LLM-Diffusion architecture for full-length song generation. The core methodological contribution is a hierarchical representation modeling strategy that decouples global semantic planning (via a Mixed Semantic LM predicting mixed tokens) from track-specific acoustic refinement (via a Track-Specific LM predicting parallel vocal and accompaniment tokens). This addresses the trade-off in existing LLM-based systems between maintaining vocal-instrument harmony and capturing fine-grained acoustic details. A significant portion of the contribution lies in the training paradigm: an aesthetics-guided three-stage process involving pre-training with musicality-tier conditions, decoupled progressive post-training (SFT, offline DPO for controllability, semi-online DPO for musicality), and modular extension. The use of an automated music aesthetic evaluation framework to guide data filtering and preference alignment is a notable technical innovation, aiming to mitigate gradient conflicts and reward hacking common in multi-objective alignment.
Results & evaluation
The evaluation is comprehensive, featuring both subjective (expert MOS across six dimensions) and objective (PER, Gemini-based prompt alignment) metrics. The paper compares LeVo 2 against leading commercial systems (Suno v5, Mureka v8) and open-source baselines (YuE, DiffRhythm 2, ACE-Step 1.5). Results indicate that LeVo 2 outperforms all open-source baselines and approaches commercial systems in key metrics like Melody and Arrangement. The ablation studies effectively validate the contribution of each training stage, particularly demonstrating that the decoupled progressive post-training strategy yields better results than single-dimension or mixed multi-dimension optimization. The inclusion of a semi-online DPO stage is well-supported by ablations showing its ability to push performance beyond static offline data limits.
Reproducibility
The authors provide inference code and full model weights on GitHub, which significantly enhances reproducibility. The paper details the model architecture (4B parameters for LeLM, 700M for diffusion decoder), training steps, and hardware setup (64 H20 GPUs). The use of open-source components like MuCodec and Qwen2-tokenizer aids in replication. However, the specific implementation details of the "automated music aesthetic evaluation framework" and the exact filtering thresholds for the semi-online DPO loop are somewhat high-level, which might require careful engineering to replicate exactly.
Limitations
The paper acknowledges that commercial systems still hold an advantage in some metrics, suggesting room for improvement. The reliance on an automated aesthetic evaluation framework for training data filtering and DPO reward modeling introduces potential biases or inaccuracies inherent in the evaluator model (MuQ-based). Furthermore, the "semi-online" DPO, while effective, still relies on periodic updates rather than true online RL, which may limit the extent of distribution shift handling. The model's performance on non-Chinese/non-English languages is not explicitly detailed, though English is tested.
Broader impact
LeVo 2 represents a significant step forward in controllable, high-fidelity song generation, bridging the gap between open-source research and commercial capabilities. By providing open weights and code, it fosters further research in music AI. The decoupled alignment strategy offers a generalizable framework for multi-objective optimization in generative models, potentially applicable to other multimodal domains. However, the ease of generating high-quality songs also raises concerns regarding copyright infringement, deepfakes, and the displacement of human musicians, necessitating responsible deployment guidelines. LeVo 2 introduces a hierarchical LLM-Diffusion framework with a decoupled, aesthetics-guided progressive post-training strategy that effectively balances global musical coherence with track-specific acoustic fidelity, achieving state-of-the-art performance among open-source models.
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