Title: MMaDA: Multimodal Large Diffusion Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Unified Diffusion Foundation Model

Reinforcement Learning

LLM Reasoning

Multimodal Understanding

Image Generation
Abstract: We introduce MMaDA, a novel class of multimodal diffusion foundation models designed to achieve superior performance across diverse domains such as textual reasoning, multimodal understanding, and text-to-image generation. The approach is distinguished by three key innovations: (i) MMaDA adopts a unified diffusion architecture with a shared probabilistic formulation and a modality-agnostic design, eliminating the need for modality-specific components. This architecture ensures seamless integration and processing across different data types. (ii) We implement a mixed long chain-of-thought (CoT) fine-tuning strategy that curates a unified CoT format across modalities. By aligning reasoning processes between textual and visual domains, this strategy facilitates cold-start training for the final reinforcement learning (RL) stage, thereby enhancing the model's ability to handle complex tasks from the outset. (iii) We propose UniGRPO, a unified policy-gradient-based RL algorithm specifically tailored for diffusion foundation models. Utilizing diversified reward modeling, UniGRPO unifies post-training across both reasoning and generation tasks, ensuring consistent performance improvements. Experimental results demonstrate that MMaDA-8B exhibits strong generalization capabilities as a unified multimodal foundation model. It surpasses powerful models like LLaMA-3-7B and Qwen2-7B in textual reasoning, outperforms Show-o and SEED-X in multimodal understanding, and excels over SDXL and Janus in text-to-image generation. These achievements highlight MMaDA's effectiveness in bridging the gap between pretraining and post-training within unified diffusion architectures, providing a comprehensive framework for future research and development. We open-source our code and trained models at: https://github.com/Gen-Verse/MMaDA


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates a uniform discrete diffusion architecture for unified Multimodal Large Language Models (MLLMs), with a particular emphasis on post-training techniques. The authors curate multimodal long-CoT data and propose a GRPO algorithm specifically tailored for diffusion-based models to enhance the model’s reasoning capabilities. Experimental results show that the model achieves strong performance across a range of downstream multimodal generation and understanding tasks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. This paper explores a new architecture for unified MLLMs, addressing a topic of significant interest to the research community.\n2. The proposed UniGRPO adapts the autoregressive GRPO algorithm to diffusion models, demonstrating improved effectiveness and stability compared to the alternative diff-GRPO.\n\n**Weaknesses:**\n1. The paper lacks an in-depth discussion of the unique advantages offered by the proposed diffusion-based architecture compared to AR-based architectures. While the authors claim that MMaDA is distinguished by its \"shared probabilistic formulation and modality-agnostic architecture,\" these characteristics are also present in AR-based unified MLLMs, making the claimed distinction less convincing.\n2. The comparison with SOTA models is incomplete in Tables 2 and 3. Notably, several relevant models—such as VILA-U [1], SynerGen-VL [2], Liquid [3], and MetaMorph [4]—are missing.\n3. The writing could be improved. The manuscript relies heavily on bullet points and bolded phrases, which disrupts the overall flow and coherence of the narrative. This formatting choice makes the paper read more like a collection of notes rather than a cohesive essay, ultimately detracting from the clarity and readability of the work.\n\n[1] VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation \\\n[2] SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding \\\n[3] Liquid: Language Models are Scalable and Unified Multi-modal Generators \\\n[4] MetaMorph: Multimodal Understanding and Generation via Instruction Tuning\n\nQUESTIONS:\nTo what extent does UniGRPO improve the model’s understanding and generation performance? It would be helpful if the authors could compare the performance of MMaDA before and after stage3 training, specifically with respect to the metrics presented in Tables 2 and 3. Besides, the authors are encouraged to provide a direct comparison with diff-GRPO under the same settings. This would more clearly demonstrate the specific advantages of UniGRPO.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-10"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nMMaDA is a large-scale multimodal diffusion model emerging from a systematic design space analysis. Built on a unified probabilistic architecture, it provides a coherent framework for diverse modalities. Its training employs a three-stage strategy: multimodal pre-training, mixed long chain of thought finetuning, and refinement via the novel UniGRPO reinforcement learning framework. Experiments demonstrate MMaDA achieves superior and balanced performance across text generation, multimodal understanding, and text-to-image generation.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- The paper is well-structured, and the figures are clear and informative, effectively supporting the authors’ arguments.\n- Improving diffusion-based language models like Dream and LLaDA for the multimodal domain is both timely and of high academic value.\n- The proposed three-stage training strategy proves effective, enabling MMaDA to achieve strong and well-balanced performance on three distinct tasks: text generation, multimodal understanding, and text-to-image generation.\n\n**Weaknesses**\n- The abstract and introduction claim that MMaDA attains state-of-the-art performance across multiple domains; this claim appears somewhat overstated. The comparative baselines, such as LLaVA-v1.5, are relatively outdated. In addition, Figure 2 mentions Janus Pro, yet this model is absent from the tables. For a fair evaluation, please include stronger and more recent baselines and report the parameter counts of compared models. For instance, Dream 7B for text generation, Janus-Pro for text-to-image generation.\n- Several baseline results seem inconsistent. For example, Qwen2 instruct model is reported to achieve 80.2 on GSM8k in Table 4, whereas the original Qwen2 instruct model paper reports 85.7. Please verify these results and either correct them or clarify the discrepancy.\n- The Related Work section is brief and somewhat overlaps with the second paragraph of the introduction. I recommend expanding this section—perhaps by condensing the more detailed discussion currently relegated to the appendix—so that the main text provides a concise yet comprehensive overview of relevant prior work.\n- In Table 4, the comparison with baselines seems unfair. MMaDA undergoes mixed long-cot post-training and UniGRPO training, whereas the baselines appear to be solely pre-trained LLMs, making the comparison potentially misleading.\n- In Table 5, the metrics for each task are limited to only one. It would be beneficial to include additional metrics to provide a more comprehensive evaluation. For example, in the text generation task, MMLU is relatively simple as it only requires validating options. Expanding the metrics would enhance the robustness of the analysis. \n- Some typos in the text. For example, in Figure 1, \"Discreate Diffusion\" should be \"Discrete Diffusion\". Additionally, the quotation marks for \"reasoning_process\" on line 108 appear to be incorrectly formatted.\n\nQUESTIONS:\n- What do \"higher-order solvers\" refer to in line 294?\n- Table 5 needs additional metrics for a comprehensive evaluation. Current analysis is insufficient—diffusion-based language models like Dream and LLaDA are sensitive to reduced sampling steps on GSM8K, showing significant performance drops. Concluding that \"sampling efficiency of diffusion-based language models is good\" based solely on MMLU results is somewhat misleading. Please add more metrics or revise the claims.\n- Which sampling algorithm is used for MMaDA performance evaluation: semi-AR or original diffusion method? \n\nI will be happy to raise my score if all my concerns are resolved.\n\nLIMITATIONS:\nNo. The authors only mention that the current model size is limited. However, many well-performing models are around 8B parameters (e.g., Qwen2.5 VL 7B), making this limitation claim vague. Please see the questions and weaknesses noted above.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents MMaDA, an 8-billion-parameter multimodal large diffusion language model that unifies textual reasoning, multimodal understanding, and text-to-image generation inside a single discrete-diffusion Transformer.\nKey ingredients are (i) a modality-agnostic diffusion backbone, (ii) Mixed Long-CoT supervised fine-tuning that supplies cross-modal chain-of-thought traces, and (iii) UniGRPO, a diffusion-friendly policy-gradient algorithm that combines KL regularisation, mask-ratio scheduling, and multiple modality-specific rewards in one objective.\nTrained for three stages on 64 A100-80 GB GPUs (≈ 650 k steps), MMaDA achieves: \n\n1. Multimodal understanding gains over strong baselines on POPE, VQAv2, MMMU, and other suites. \n2. Best CLIP-Score 32.5 and ImageReward 1.15 among all unified models tested. \n3. Competitive textual reasoning (e.g., GSM8K 73.4) while retaining diffusion’s parallel sampling; only 15–50 denoising steps suffice for good image quality.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n* A single diffusion language model handles generation and reasoning across modalities, eliminating separate AR blocks or specialised heads.\n* The model outperforms or matches state-of-the-art unified systems on eight vision–language and six language benchmarks, demonstrating versatility.\n* Three clearly separated stages and an ablation (Table 6) show how Mixed Long-CoT and UniGRPO work.\n\n# Weaknesses\n* KL constraint, uniform-ratio mask scheduling, and per-modality reward heads are all active at once; hyperparameters are numerous and strongly coupled. Current ablations toggle only the whole UniGRPO block, leaving the individual contribution and stability of each reward unclear.\n* The CoT traces are generated and filtered by existing LLM/VLMs (GPT 4.1), raising questions about scalability to domains or languages where such models underperform.\n* Using a fixed 32×32 VQ grid (f = 16) may bottleneck high-resolution details and limit future scaling compared with more expressive continuous encoders.\n* Because UniGRPO mixes correctness, CLIP score, ImageReward, and format rewards, improper balancing could harm certain downstream tasks; stability analysis across seeds/tasks is missing.\n\nQUESTIONS:\n* Could you isolate each UniGRPO component—KL term, mask-ratio schedule, text vs image-reward heads?\n* How sensitive is performance to the relative weights of correctness, CLIP, and ImageReward signals?\n* If the curation LLM/VLM is weaker (e.g., in low-resource languages), does Mixed Long-CoT still improve alignment, or does it propagate errors? Any plans for model-generated self-CoT data to reduce this dependency?\n* What are the trade-offs of retaining the MAGVIT-v2 VQ tokenizer versus adopting higher-resolution or learned continuous encoders, and how might that affect future MMaDA scaling?\n\nLIMITATIONS:\nPlease refer to the weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces MMaDA, a unified framework that bridges pretraining and post-training for diffusion-based multimodal foundation models. The work addresses a critical gap in existing unified multimodal models, which primarily focus on architectural design and pretraining strategies while neglecting post-training methodologies, particularly in non-autoregressive settings.\n\nThe paper makes three main contributions: (1) a unified diffusion architecture that eliminates modality-specific components by using discrete tokenization and masked token prediction for both text and images, (2) a mixed long CoT finetuning strategy that aligns reasoning across modalities using a unified format, and (3) UniGRPO, a novel reinforcement learning algorithm adapted for diffusion models that addresses specific challenges like local masking dependency and non-autoregressive sequence-level likelihoods. The model demonstrates competitive performance across textual reasoning, multimodal understanding, and text-to-image generation tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The adaptation of GRPO to diffusion models is technically sound and addresses challenges in applying RL to non-autoregressive models.\n\n2. The unified probabilistic formulation using discrete diffusion for both text and images is well-motivated.\n\n3. Strong empirical results across multiple domains, achieving state-of-the-art performance on several benchmarks while maintaining competitive results on others.\n\n4. The demonstration that diffusion models can serve as general-purpose language models is significant.\n\nWeakness:\n\n1. The paper lacks comprehensive ablation studies to isolate the contribution of each component (unified architecture vs. mixed CoT vs. UniGRPO). With three major innovations introduced simultaneously, it's unclear which components are most critical for the observed performance gains and which might be incremental improvements. Is it possible to provide systematic ablation experiments that isolate the individual contributions of the unified diffusion architecture, the mixed long-CoT finetuning, and the UniGRPO algorithm, in some creative experimental design?\n\nQUESTIONS:\nGiven that the model is limited to 8B parameters, how is the approach expected to scale to larger model sizes (20B+)? Are there any fundamental barriers to scaling? Also, are there some insights or conditions where MMaDA performs poorly compared to other models? Please also refer to weakness.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}