MMaDA: Multimodal Large Diffusion Language Models
Authors: Ling Yang
Ye Tian
Bowen Li
Xinchen Zhang
Ke Shen
Yunhai Tong
Mengdi Wang
Venue: NeurIPS 2025 poster ()
Keywords: Unified Diffusion Foundation Model
Reinforcement Learning
LLM Reasoning
Multimodal Understanding
Image Generation
Primary area: deep_learning

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

MMaDA: Multimodal Large Diffusion Language Models
Ling Yangl Ye Tian"; Bowen Li,, Xinchen Zhang
Ke Shen,* Yunhai Tong', Mengdi Wang"t
Princeton University "Peking University Tsinghua University "ByteDance Seed
Abstract
We introduce MMADA, 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 shared probabilistic formulation and moodality-agnossii design, eliminating the need for poddality-secciffe components. This architecture ensures seamless integration and processing across different data types. (ii) We implement a mixed long taain-of-thouse
(Co'T) fine-tuning strategy that curates a unified CoT format across modalities. By aligning reasoning processes between textual and vi- sual domains, this strategy facilitates cold-start training for the final reinforcement learning (RL) stage, thereby enhancing the model's ability handle complex tasks from the outset. (iii) We propose UniGRPO, a unified policy-gradient-basee RL al- gorithm specifically tailored for diffusion foundation models. Utilizing diversified reward modeling, UniGRPO unifies post-training across both reasoning and gener- ation tasks, ensuring consistent performance improvements. Experimental results demonstrate that MMaDA-88 exhibits strong generalization capabilities as uni- fied multimodal foundation model. surpasses powerful models like LLaMA-3-7B and Qwen2-7B in textual reasoning, outperforms Show-o and SEED-X in multi- modal understanding. and excels over SDXL and Janus text-to-image 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: thub. com/
com/Gen- Gen Verse/MKaDA
Introduction
Large language models (LLMM)) have revolutionized natural language processing (NLP) by achieving state-of-the-art performance in diverse tasks, from text generation (e.g.. ChatGPT [33 to complex reasoning ge.g., DeepSeek-RR 4). Inspired by their success, the research community has extended LLMs to the multimodal domain, giving rise to multimodal large language models (MLLMs) or vision- language models (VLMs) [4] such as GPT-4 [15] and Gemini [12]. These models aim to provide unified framework for both understanding and generating across heterogeneous modalities text, images, and beyond.
Early multimodal approaches combined language models with diffusion models 16 to handle discrete (e.g., text) and continuous (e.g.. image) modalities separately. Subsequent autoregressive (AR) methods simplified architectures by training a single transformer with next-token prediction,
"quual Contribution.
Corresponding Authors. Contact: yangling0818e 163.com, mengdiw @ princeton.edu
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Another line of work leverages modality training objectives within a shared architecture: for example, Show-o 2 and Transfusion [2]] combine autoregressive and diffusion modeling for modeling textual and visual semantics, respectively.
unifying discrete and continuous generation in a single model [22 9 10 13
14
Table 1:
Specific design choices employed by different unified multimodal foundation model fam- ilies, including their core loss functions. The next-token prediction loss is defined as CNTP = Ez, [olo (z; x=ci],, representing the standard negative loglliielihood of generating the next token I; conditioned on its preceding context Fei. The training objective for continuous diffusion models is given by Liir
[lle 60 l, e)][3], where Ie denotes the noised version of the original data Ty at timestep t, and e is an optional conditioning signal. The model learns to predict the noise added to the data, thereby enabling the reconstruction of xo through an iterative denoising process defined as 1 = Fo( t). The discrete diffusion (masked
token prediction) loss is given by Lpar-dise = E M
1 Σres log po (o maskes)) which measures
the average negative log-liiel of correctly predicting the original discrete tokens 2 at positions
masked by the set M, given the visible context provided by the masked sequence Z (M)
masked
AR (One Model)
AR Diffusion (Two Models)
AR Diffusion (One Model)
Ours MMADA (One Model)
Network Architecture
Langunge
AR AR
AR Diffusion
AR Diffusson
Diffasion Diffisioo
Vision
Pre-training
Loss for Language
EXTr Eyr
CNTP Cost-oo
CXTP Cottern or Cpis-ai
Coat
Loss for Vision
Sampling
Langunge
I: n Po(x, Ies)) ay n Pa(R, (tee))
ay Polis lane)) Me-1 Fuliec))
xy nu Po(zz ine 1x-1 Fo(ite))
Ze-1 = Fela,, 0
Vision
= Fulite Q
Language Scheduler
Semi-AR Remask 12
Vision Scheduler
DDPM
DDPM Cosine
Cosine Remask
Post-Training
Language CoT
Mixed Long- CoT
Language
UniGRPO Divensifid
Vision CoT
Mixed Long-CoT
Vision RL
DPO 25
UniGRPO with Diversifed RM
Tasks
Und. (with Reasoning)
V(O)
((K
v(O)
Image Gen. (with Reasoning)
(XK)
(()
Texi (with Reasoning)
X(X)
X(O)
X(X)
VI2
Representative Models
Emu3
25
Janus
B3
DreamLLM [16]
Transfusion [21] Show-o 20
MMADA (Ours)
recent advancements have explored diffusion- liffusiin-bbsed architectures for global context model- ing and parallel generation 21. existing unified multimodal foundation models predominantly focus on model architecture design and pretraining strategies, leaving a critical gap in the exploration of post-training methodologies, particularly in non-autoregressive settings.
Although
To address this gap, we systematically investigate the design space for unified multimodal diffusion foundation models, introducing a novel framework that advances both architectural and training paradigms (a comprehensive comparison in Table This work bridges the gap between pretraining and post-training in unified multimodal diffusion models, offering a holistic framework for future research in this emerging field. Our contributions can be summarized as:
Unified Diffusion Foundation Architecture: We propose MMADA, a class of diffusion-basee models that extend traditional generators into generalist task solvers via a shared probabilis- tic formulation and modality-agnosti architecture. This design eliminates coddllty-specciff components while maintaining strong performance across tasks.
Mixed Long-CoT Post-Training We introduce mixed long chain-of-thought (CoT) finetun- ing to enable cold-start training. By curating a unified CoT format across tasks, we align reasoning processes between modalities (e.g., textual and visual), fostering cross-modal synergy and learning intermediate reasoning before final output generation.
Unified Reinforcement Learning (UniGRPO): We develop a unified diffusion-centric reinforcement learning algorithm (UniGRPO) tailored for multimodal generation. This approach leverages diversified reward modeling to enhance the model's 's ability to perform complex reasoning and maintain factual consistency in generation.

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Task Textual Reasoning
Answers from Other Models
Answer from MMoDA
<ihhinkss
Question:
valuee (sin' p cos P) giren ton 1/2,
these steps:
Exprres s m tan B// tar" [.-m (3/2044 (1P)" 1/V8
Given that
tan
cor = 11/11++tnn 2/V3
2. ""Calculate each term
staf (1/V))
where is an angle, determine the exact value of the following expression:
Show-o, kmmnd and Janus Pro 7B do not support this task
s Roos - (3/(1)) (2/v8) 3: (2/5) 6/5
coss p- (2/+5) (1/5) 16/5
Suhhttittt simplity:=*
sin' B
sin oos
Answer: 1.1/5
B- B.os 1-4coss 1/5 6/5 16/5 11/5
</thinno>
The value
Task Multimodal Reasoning
Answers from Models
Answer frum MMaDA
<think>
To deteemine the mescure snnlle 2, we seed to analgee the givi information and use the propertins of parsilal lines, and perpendicul trity.
Show-a:
Angle 2 measures 52 degrees.
1.*"identify the given information"*
Eman3:
is parsilet
intersections line EF with lines ED mopscttoely
and
Anglo is equal to angle 1 due to the parallel lines
Question:
All and CD Since angle menearrs degrevs ande also mesoures 52 degrees.
-Line EG perpendicaler lire EE with point
In the dogram provided, lines and CD are parallel The time EF intersects AB point E and CD at point F. From point R, perpendicular EG meeting ED point angle is 52 degrees, what the mexzure of angle 2?
the perpendiculat
Janus Pro 7B:
-Angle messures 52 digress.
intersects line CD point
Since angles equal.
parallel line correspondng
="nnderstand the relationship between the angles!"
EF, that
Thersfore, angle angle
-Therffom, angle 2 angle 1 degress..
-Since EG perpendicular to iFFand FBG is degrees.
angle degrees.
degross, angle 2 also
3. "."ettemmnne the measure of angle 2:
-Angle 2 90 degrees 52 degrees 30 degrees.
The answerts
So, the measure of angle 2 (3)) </think>
The measure angle 30 degrees.
Task 3: World Knowledge-Aware Text- to IImge Generation
Images from Other Models
CoT and Image from MMaDA
Show-o
Emu3
Jonus Pro 78
<thinke>
The descriptin states thatthe most popular animal among the people Istarbal is the ost. This is becouse cats are pope lar animal for compationship and people in the city.
Prompt:
most popular animal among the people of Istambu..
</thinks
close-up cat light brown and fur pattern.
Figure I: Qualitative comparison across three tasks (more results in Appendix F and Appendix
G
State-of-the-Art Performance: MMADA achieves superior and balanced performance across three critical tasks: texiual reasoning, multimodal understanding, and text-to-imaae genera- tion. Notably, outperforms both autoregressive and diffusion-basse baselines in terms of accuracy, efficiency, and task adaptability.
2 Related Work
29 have focused on developing unified multimodal foundation models that excel in both understanding and generation. Approaches such as SEED- X [19 and DreamLLMM [16), along with others [H144) represent all modalities as a series of tokens and employ unified transformer architecture to train the entire system end-to-end. For example, Emu3 (25) trains single transformer from scratch using mixture of multimodal tokenized sequences, optimized solely with next-token prediction. While these unified autoregressive models show promise, they can struggle with visual generation tasks. Transfusion [2I] and Show-o [20] employ autoregressive modeling for text generation and diffusion modeling for visual generation. Similarly, VAR-GPT 30 models visual understanding and generation with autoregressive modeling and visual autoregressive modeling (VAR 311, respectively. Nevertheless, they mainly focus on pretraining strategies. Exploring effective post-training designs is still lacking for existing unified multimodal foundation models. (More related works can be found in Appendix AA
Recently, numerous studies 19 26
16
3 MMADA: Multimodal Large Diffusion Language Models
3.1 Pretraining with Unified Diffusion Architecture and Objective
Data Tokenization To establish a unified modeling framework capable of processing both textual and visual data, we adopt a consistent discrete tokenization strategy across both modalities. This design enables the model to operate under single modeling objective, i.e., the prediction of discrete masked tokens. For text tokenization, we utilize the tokenizer from LLaDA (2]. which serves as the backbone for our MMADA model. For image tokenization, we leverage the pretrained image quantizer adopted from Show-o [20). which is based on the MAGVIT-v2 [24] architecture and converts raw
3

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Three Diverse Tasks:
Mixed Long-CoT T Data:
UniGRPO Diversified Rewards:
Jnnasavo (P) En--otio) (T(""((()
(o)) PP()
Training Stage
Language Modeling
Intensive Tasks
Reatoning-
Mixed Long- bata with Insfied F cevezr
Textual Reasoning
Multimodal Understanding
SeepSeak-At LMM-RL
C Date
Primury
R
Correctness, Format,
Multimedal Reasoning
World inowledge-
guistl Correctness, CL.P--cooe, Format,
cwure TZI Sen:
Varsfy and
Visual Generation
GPT-4.11
Reformet
Text-to- -m-eg Generation RTZl: CLIP-Scoree ImugeReward....
Stage1: Pretraining
Stage2: Mixed Long-CoT Finetuning
Stage3: UniGRPO Training
Stage3: UniGRPO Training
Discrete Diffusion Generation
Uniformly Random Masking in Iterations
Iterationn 1, p
(g(P.)))
Iterative Denoising:
Iteration 1, p 0.50
Image/Ten Prompt Image/ Text Resporde
Iteration 1, p 0.75
Remask Response for Masked Log Probs
Sequence Log Probs by Averaging
Tip (o.
Masked Token nettisa))
Input Taken
Completion Taken
Remask Token
Textual Reasoning
Multinodal Reasoning
Text-to-Innen to-Image Generation
Inference Stage
Q: How nony fou-tourths are there 7/2?
Q: What the cat the Image doing?
Generate about famous landnark building Europe.
Text Tokenizer
Image Tokenizer
Text
Text Tokenizer
Reason and Generate with World Knswledge
Toleniizer
MLLM (Discrete Diffusion)
Asking question (.9) the </thisko
rthinks _<//hinno Eiffel Towar reaching for
6 pushing. </thinio pushing small
shopping
Figure 2: An overview of MMADA pipeline.
image pixels into sequences of discrete semantic tokens. Given an input image with dimensions H x W, the encoder generates a token map with dimensions !! W where / represents the J x f.
downsampling factor. In this implementation, we employ a downsampling factor of f = 16 and a codebook size of 8192. This configuration transforms 512 x 512 pixel image into sequence of 32 x = 1024 discrete tokens. The transformed discrete image tokens are used in both understanding and generation modeling tasks.
Unified Probabilistic Formulation for Pretraining Recent unified multimodal frameworks aim to integrate multiple modeling objectives- suchh autoregressive generation and diffusion-bait based denoising- intt a single architecture for joint understanding and generation tasks (20] (1] (see pre- liminaries in Appendix [B.]. However, these approaches often introduce complex hybrid mechanisms that hinder model efficiency and coherence. In contrast, we propose a streamlined framework that not only simplifies the architectural complexity but also introduces a unified diffusion objective model both visual and textual modalities under a shared probabilistic formulation. By aligning the noise corruption and semantic recovery processes across modalities, we enable more effective cross-modal interactions during pretraining, facilitating seamless integration of heterogeneous data sources.
Specifically, we formulate MMADA as mask token predictor (for both image and text tokens), parametric model pe(+(z;) that takes x, as input and predicts all masked tokens simultancously. The model is trained with unified cross-entropy loss computed only on the masked image/text tokens:
L
Cunity (0) Ey ar
1 Σ i
M [MASK]] log pa (2) (2612z))
(I)
where zo is ground truth, the timestep / is sampled uniformly from [0, 1]. and x, is obtained by applying the forward diffusion process to 2g. I[.] denotes the indicator function to ensure that the loss is computed only over the masked tokens. Specific pretraining tasks detailed in Section 4

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3.2 Post- most-Training with Mixed Long-CoT Finetuning
Cold Start Long-CoT Data Curation We investigate how CoT mechanisms can enhance post-training for our unified multimodal diffusion framework and observe their effectiveness in promoting cross-modal synergies. To this end. we curate a compact dataset of long CoT trajectories across three core tasks: textual reasoning. multimodal reasoning, and lext-lo-image generation. This dataset enables stable post-training of our pretrained MMADA model through the following principles:
Unified CoT Format: A critical challenge in vision- vision-language foundation models is the heterogeneity output formats across tasks (c.g., text vs. image generation). We propose a task-agnostic CoT format:
<special_token>> < reasoning_process
<special_
result
The "<reasoninggproceeseet encodes step-by-step reasoning trajectories preceding the final output. This unified structure bridges modality-specin outputs and facilitates knowl- edge transfer between tasks. For instance, enhanced textual reasoning capabilities directly improve the realism of generated images by aligning semantic logic with visual synthesis.
Diversity, Complexity, and Accuracy: We leverage open-source large language and visionllanuusn models (LLM/VLMs) generate diverse reasoning trajectories across tasks (in Fig. 2). To ensure quality, we employ state-of-the-art models as verifiers to filter inaccurate shallow reasoning, selecting only high-quality, long-form CoT samples. Unlike prior unified models that focus on generic understanding and generation, our MMADA is explicitly designed for:
1. Reassoiing-mtennivee intensive tasks (e.g., mathematical problem-solving), and
2. Worldd-knowledge-ana text-to-image generation, where factual consistency is critical.
Mixed Long-CoT Finetuning Leveraging our unified diffusion architecture and probabilistic formulation, we develop a mixed-task long-CoT finetuning strategy to jointly optimize the model across heterogeneous tasks. This approach not only enhances task-specific capabilities but also creates strong initialization for subsequent reinforcement learning (RI.) stages. The training process follows these steps:
1. Prompt Preservation and Token Masking: We retain the original prompt Po and indepen- dently mask tokens in the result (ro), denoted as rz.
2. Joint Input and Loss Computation: The concatenated input [Po, ri] is fed into our pre- trained mask predictor to compute the loss. This enables the model to reconstruct masked regions (ro) using contextual information from both the prompt and corrupted result.
The objective function is defined as:
L' Σ T [MASK] log (rooloo, re)
Mix = Po.roro
(2)
where L' denotes the sequence length. Here, [po, ro] and [po. correspond to the clean data zo and its noisy counterpart ze, respectively. This formulation ensures the model learns to recover masked tokens while maintaining alignment with the original prompt and task-specific reasoning logic.
3.3 Post- West-Training with Unified Reinforcement Learning
3.3.1 Unified GRPO for Diffusion Foundation Models
With our mixed long-Col fine-tuning, MMADA demonstrates the ability generate unified and coherent reasoning chains prior to final outputs. To further enhance its performance on knowledge-intensive tasks and complex reasoning/generationn scenarios, we propose UniGRPO, novel based reinforcement learning algorithm tailored for diffusion foundation models. This approach enables diffusion-centric RL training framework that unifies task-specific objectives across diverse modalities and reasoning paradigms. The method is structured into two core components: (1) unified mathematical formulation for diffusion-based RI,, and (2) diversified reward modeling to align policy gradients with task-specific rewards.
5

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Challenges in Adapting Autoregressive GRPO to Diffusion Models The original GRPO B3 relies on computing token-level log likeeliiooooos To(oi,, q, Oi,ct) and sequence-lever probabilities Ti and fref (preliminary in Appendix B.2). In autoregressive (AR) LLMs, these metrics are efficiently derived via the chain rule of generation. However, diffusion models introduce three critical challenges: (1) Local Masking Dependency: Token-level log-likelihoods log ( 01,ct) are only valid within masked regions during the diffusion process, unlike AR models where all tokens are valid. (2) Mask Ratio Sensitivity: A uniform mask ratio must be sampled for the response segment to approximate the policy distribution Te, as diffusion dynamics depend on masking patterns. (3) Non-Autoregressive Sequence-Leve Likelihoods: The sequence level log- likelihhod cannot be directly accumulated from token-level probabilities due to the absence an autoregressive chain rule in diffusion models. Prior approaches address these issues with suboptimal strategies. LLaDA [2 employs Monte Carlo sampling over numerous mask ratios (e.g., 128 samples), incurring high computational costs for on-policy RI. d1 34 fixes the mask ratio and randomizes question masking, which reduces noise diversity and ignores the multi-step denoising nature of diffusion.
Unified Formulation for Diffusion GRPO To overcome these limitations, introduce Uni- GRPO, a computationally efficient approximation algorithm designed for diffusion architectures. Given batch of responses {or for query 9. each response is fixed during gradient updates to ensure stable policy evaluation. We highlight three critical points of our UniGRPO as:
1. Structured Noising Strategy: For each og, we sample masking ratio E [0, 1] uniformly and construct a perturbed version Öi,p by replacing tokens with [MASK]. The random seed for P: varies across gradient steps, ensuring diverse masking patterns during training.
2. Efficient Log-Likelihood Approximation: We define the expected per-token log-likelihood under the perturbed distribution as:
(o, g, o, p.) [0. [o,.,
[MASK]] log pa (or,t.p 14)]
(3)
The sequence-level log- likelikelihood is then approximated by averaging over masked tokens:
88
Σ O,
log Po
where denotes the number of masked tokens.
M
3. Policy Gradient Objective: The per-token reward is computed as the ratio between current and old policy likelihoods: (0) T (or (g. The final UniGRPO objective integrates
(o, p..)
clipped surrogate rewards and KL regularization:
(Un)GRPO())
min (1),
00
clip clip(ri,,(0) -,,
- -,) Are)
3DK (
(5)
where A denotes the advantage estimate, E controls the clipping range, and |3 balances the KL. divergence penalty.
For further details on the UniGRPO algorithm, please refer Appendix D and Appendix E.2
3.3.2 Diversified Reward Modeling
We further simplify the optimization objective of UniGRPO (Eq. 5 as follows:
Remark 1.
General optimization objective of UniGRPO:
JUmGRPO (8) (oo=ss( (o)) 3P()))
(6)
where (o) denotes the reward obtained from the model-generated response o, P(.) is the penalty
term, which denotes the KI. divergence as specified in Eq. (5). This is unified rule-based reward system, where RUMi(.) can be instantiated with diverse rewards for different tasks. To address

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Table 2: Evaluation on Multimodal Understanding Benchmarks.
Model
POPE
MME
Flickr30k*
vQAv2
GQA
MMMU1
MMB
SEED f
Understanding-Out
LLaVA-v1.5
38
85.9
1510.7
78.5
62.0
35.4
64.3
58.6
InstructBLIP
39
78.9
1212.8
49.5
Qwen-VL-Chal VL-Chal
40)
1487.5
78.2
57.5
60.6
58.2
mPLUG-Owl2
85.8
1450.2
79.4
56.1
LLaVA-Phi
85.0
1335.1
71.4
59.8
Unified Understanding Generation
DreamI.IM 16
72.9
SEED-X 19
84.2
1435.7
52.3
47.9
35.6
Chamelcon
74.7
66.0
LWW
75.2
948.4
55.8
44.8
Emu 11
77.4
57.2
Show-o 20
80.0
1097.2
62.5
69.4
58.0
26.7
Gemini-Nann 1aattil
12
62.7
26.3
MMaDA (Ours)
86.1
1410.7
67.6
76.7
61.3
30.2
68.5
64.2
the varied requirements of different tasks, we have defined range of rewards under the unified formulation Eq. 6 providing tailored RL optimization directions for each task branch, We mainly adopt three types of rewards:
Textual Reasoning Rewards: We apply UniGRPO on the training split of the GSM8K [35 dataset and define composite reward. This includes Correctness Reward of 2.0 for a correct answer, and Format Reward of 0.5 if the response adheres to our predefined format: "<think>... </think>".
Multimodal Reasoning Rewards: For mathematical tasks such GeoQA 36 and CLEVR [37]. we adopt the same Correctness and Format Rewards as in textual rea- soning. In addition, for caption-based tasks, we further introduce CLIP Reward of 0.1 Cimi(iiage,, text), where the original CLIP score measuring text-image alignment is scaled by 0.1 to balance its influence.
Text-to-Image Generation Rewards: For image generation tasks, incorporate the same CLIP Reward to assess text-image semantic alignment, alongside an Image Reward that reflects human preference scores. Both rewards scaled by factor of 0.1 ensure balanced contribution during optimization.
4 Experiments
Datasets To train MMADA, utilized diverse range of datasets tailored for corresponding training stages as follows: (1) Foundational Language and Multimodal Data: For basic text generation capabilities, adopt the RefinedWeh 43 dataset. For multimodal understanding and generation tasks, we incorporate image-text datasets including ImageNet- 1 (44), CC12M [45). SA1B [46),
LAION-aestherics- [47]. and JourneyDB 48
(2) Instruction Tuning Data: To enhance
instruction-ffllloinng capabilities, we use Alpaca
49 for textual instructions and VLa-1- (38) for
visual instruction tuning. (3) Reasoning Data: For Mixed Long-CoT finetuning, curated a diverse set of reasoning datasets. For textual mathematical and logical reasoning, we employed LIMO (50). s1k (51), OpenThhuuhtn [52], and AceMath Instrutt [53]. For multimodal reasoning, we used the LMM-R1 (54) model to generate responses on GeoQA (36] and CLEVR [37]. and retained correctly answered instances. Additionally, for world knowledge-awd aware image generation, we used GPT-4. to synthesize factual item item- denerriesriiiinnn pairs spanning science, culture, and landmarks, formatted into unified CoT-style traces. (4) Reinforcement Learning Data: For UniGRPO training, we adopt the original mathematical and logical datasets used in Reasoning
[5 36 37
Evaluation and Baselines evaluate our MMADA on three distinct tasks using task-specific metrics and baselines:(1) Multimodal Understanding: Following LLaVA [38] we evaluate on POPE, MME, Flickr30k, VQAv2, GQA, and MMMU, and compare against understanding-onl models (8.42)
well as unified models [19 16 8 9
20 12] (2) Image Generation: We assess generation

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Table 3: Evaluation on Image Generation Benchmarks.
Model
Wise (Natural)
Image Reward
CLIP Score
GenEval!
Single Obj.
Two Obj.
Counting
Colors
Position
Overall
Generation-Olly
0.79
0.71
SDv1.5
60
0.34
0.84
23.54
0.97
0.38
0.35
0.76
0.04
0.06
0.43
SDv2.1 60
0.30
0.95
27.41
0.98
0.51
0.44
0.85
0.07
0.17
0.50
0.77
SDXL
62
0.43
1.13
32.12
0.98
0.74
0.39
0.85
0.15
0.23
0.55
Unified Understonding Geveration
DreamL.LM 16
0.76
18.33
SEED-X 19
0.77
23.15
0.97
0.58
0.26
0.80
0.19
0.14
0.49
1WW 0
0.78
26.21
0.93
0.41
0.46
0.79
0.09
0.15
0.47
Emu 10
0.81
22.29
Janus [3]
0.16
1.03
29.45
0.97
0.68
0.30
0.84
0.46
0.42
0.61
Gemini Nano-
P
0.89
24.58
VAR-GPT
28.85
0.83
WMaD4 (Ours)
0.67
1.15
32.46
0.99
0.76
0.61
0.84
0.20
0.37
0.63
quality using 50K prompts from our test set to compute CLIP Score [56] and ImageReward [57 to evaluate textual alignment and human preference alignment. We adopt GenEval [58] for general
evaluation and WISE [59] for evaluating world knowledge- based generation, comparing against
generation-specific models [60 and unified baselines (19 16
I3 20
12
30). (3) Text
Generation: we evaluate instruction-followiin and reasoning performance on MMLU, GSM8K, and related benchmarks, comparing with LLaMA2-7B, Qwen2-7B, LLaDA-8B and Dream-7B (64 We detail our inference mechanism for text and image generation Appendix C
Implementation Details We initialize MMADA with LLADD--B-Instrucee pretrained weights 22 and an image tokenizer with Show-o's pretrained ones. We perform joint training across three stages: Stagel: The initial model is trained for 200K steps using foundational language and multimodal data, including RefinedWet for text generation, ImageNet-Ik k for class-conditional image generation, and additional image-text datasets for captioning. This is followed by another 400K steps where ImageNet is replaced with more diverse image-text pairs. Stage2: The model is then jointly trained for 50,000 steps using Instruction Tuning Data and Reasoning Data. Stage3: This final stage consists of UniGRPO training with Reinforcement Learning Data for 50,000 steps. Training is performed on 64 A100 (80GB) GPUs using global batch size of 1,280. The Adam W optimizer employed with an initial learning rate of 5e-5 and a cosine learning rate scheduler.
Multimodal Understanding Table T the multimodal understanding performance of our method on standard benchmarks, including POPE, MME, Flickr30k, VQAv2, GQA, and MMMU. For outputs from MMADA that contain reasoning traces, we use the final answer as the prediction. Compared with dedicated understanding-ont models such as LLaVA-v1.5 InstructBLIP, and Qwen-VL-Chat, our model achieves comparable or superior results across most benchmarks, despite being trained under unified objective. When compared to other unified models (e.g., SEED-X. DreamLLM, Janus, Emu3, and Show-o), our method consistently outperforms them across several benchmarks, particularly benefiting from the proposed Mixed Long-CoT Finetuning and UniGRPO Reinforcement Learning stages. Notably, this is the first demonstration of a diffusion-baas strong understanding capabilities, highlighting the potential of our unified architecture in bridging generation and understanding tasks. Qualitative results are in Appendix and Appendix
MLLM exhibiting
Text-to-Imagee Generation Table 3 presents the evaluation results on text-to-image generation benchmarks. Our model achieves the highest performance both CLIP Score and ImageReward across generation-only and unified models, attributed to the UniGRPO training stage with rewards explicitly aligned these metrics. Furthermore, our method demonstrates superior compositionality and object counting capabilities on GenEval, benefiting from the reasoning-intensin training of the understanding branch. Notably, on WISE [59] Natural benchmark, which designed to evaluate world knowledge-awan generation, our model significantly outperforms prior approaches, owing to joint training on text-based reasoning, which is typically absent in existing unified models. Qualitative results are in Appendix F and Appendix
8

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Table 4: Evaluation on LLM Benchmarks.
Model
Arch
MMLU
ARC-C
TruthfulQA
GSM8K
MATH
GPQA
I.aMA2-78
AR
45.9
46.3
39.0
14.3
3.2
25.7
LLaMA3-8B
AR
64.5
53.1
44.0
53.1
15.1
25.9
Qwen2-7B
AR
70.3
60.6
54.2
43.5
30.8
LLaDA-8B
Diffusion
65.9
47.9
46.4
70.7
27.3
26.1
Dream-7B
Diffusion
67.0
81.0
39.2
33.0
MMADA-8B (Ours)
Diffusion
68.4
57.4
43.1
73.4
36.0
28.4
Textual Reasoning Table 4 details the language modeling performance MMaDA across a range of benchmarks, encompassing general tasks such as MMLU, ARC-C, and TruthfulQA, as well as mathematical tasks including GSM8K, MATH, and GPQA. Despite being trained on limited task- specific tokens and solely open-source data, MMADA achieves comparable performance compared to strong baselines such as Qwen2-7B and LLaMA3-8B on MMLU, ARC-C, and consistently outperforms LLaDA-8BB math benchmarks. Notably, MMaDA (Ours) pioneers the joint training of a unified diffusion model for text generation, multimodal reasoning, and image generation multi-task configuration rarely explored in prior unified architectures. These results underscore the LLMs and indicate potential for stronger
viability of diffusion-bass models as future performance through enhanced text data and scaling. Qualitative results are in Appendix
5 Observations, Analysis and Conclusion
Synergy Across Various Tasks Throughout the joint training process, observe clear synergy across the three task categories- text generation, multimodal understanding, and image generation. As shown in Fig. all key performance metrics exhibit consistent im- provements during Stage 2 (training steps 120K-200K), reflecting the mutually beneficial nature of our uni- fied training framework. This synergy also evident qualitatively: as illustrated in Fig. 4 the model's re- sponses- both textual and visual become increasingly complex and coherent. Specifically, textual outputs grow more informative and logically structured, while visual understanding yields more precise and grounded descrip- tions. Consequently, for the same prompt, the gener- ated images become more accurate, detailed, and better aligned with the given instructions, demonstrating the effectiveness of joint optimization in enhancing cross modal alignment and compositionality.
Synergy Across Tasks During Joint Training
0.70
Normalbedd Pufemmnnce
0.65
0.60
0:55
Generation (MMLLU)
Multimodal Understanding (CUP Scora)
0.45
Image Goneration (Image Reword)
130K
200K
Training Steps
Figure 3: Key Performance Metries Across
Three Tasks.
Text
MMU
Gen
Q: What does greenhouse look like?
Q: Give a detailed description of image
Prompt: lorge greenhouse filled with various plants
ritoesu of nomood cuffere with a mus powen and honam fucs, supgerting bixdd netursl zod craffccll ciomonts, the testore tnvitats, with seriety stoper and foer cveate daym dimessoonntyy brohgronnd gie
Steps 10K
A:A greenhouse is covered structure ssed to grow plants, flowers, and craps.
of shopes atstrect, complic,
Steps 100K
onngged gew plants
covnee lyee of Nuri or made Form fabric milc
The posto smongel is oeating grecebuase tlc crvvornecss, The style of ihage clsce viww fresetig ce the parts and grennhoone siuutioee Thes stylt empilsstres the beauty and dierslyy plaits pronshosse's desien
withh oleas ample natural light
The
prosides contraled envennment for esseo and proeets therr from beasy made no exposerio elements, creanhosees
used for plants
warkey frats, segetablo,
Figure 4: Qualitative Illustration of Synergy Across Modalities.

---

Sampling Efficiency We identify sampling efficiency as key advantage diffusion mod- els over autoregressive (AR) approaches. Unlike AR models, which generate tokens sequentially, diffusion models enable parallel token genera- tion within each denoising step, substantially reducing the number of forward passes required. To quantify this advantage, we evaluate the per- formance of MMADA under varying numbers of denoising steps. In our setup, generation begins from 1024 [MASK] tokens. allowing up to 1024 denoising steps cteps-correspoonding to a 512 x 512 resolution image. As shown in Table 5 image generation maintains strong performance even with few 15 50 steps. For text and multimodal tasks, coherent outputs be achieved with just a quarter or half of the full steps. These results underscore the efficiency potential of liffusion-based language models and suggest that future advances in sampling techniques could further enhance their speed and quality.
Table Generation performance of MMADA under different denoising steps. Mctrris: CLIP Score for image generation and multimodal understand- ing, MMLU accuracy for text generation.
Task
Demoising Steps
Metrics*
1024
32.8
Image Generation
50
32.0
31.7
35.5
Multimodal Understanding
512
36.
1024
66.9
Text Generation
512
66.3
256
65.7
Task Extension A notable advantage of diffusion-based models their natural abil- ity to perform ispainting and extrapolation without requiring additional fine-tuning. This stems from the fact that these tasks can be formulated as masked token pre- diction problems, which are inherently in- tegrated into the training objective of dif- fusion models. While prior work such Show-o demonstrates this property only the context of image generation, MMADA ex- tends it further to multimodal understand- ing and text generation. As illustrated in Figure [ our model supports inpainting across three modalities: (i) predicting miss- ing spans in text sequences, (ii) completing answers in visual question answering given an image and partial input, and (iii) per- forming image inpainting conditioned on incomplete visual prompts. These exam- ples showcase the flexibility and general- ization capabilities of our unified diffusion architecture across diverse generation and reasoning tasks.
The capital city France
capital city of Fronce the Eiffel Tower and rich cuttural heritage.
Text
and its
rich cultural heritage.
The image features a beautiful blonde woman standing
The imoge features a beautiful womon standing in field of white flowers.
Undd
yellow racing with sleek
curves and tinted windows, parked on bustling city street.
Figure 5: Inpainting Task Extension.
6 Conclusion
This work introduces a unified diffusion foundation model, namely MMADA, that integrates textual reasoning, multimodal understanding, and generation within single probabilistic framework. To the best of our knowledge, MMADA is the first to systematically explore the design space of diffusion- based foundation models, proposing novel post-training strategies. Extensive experiments across diverse vision-langual tasks demonstrate that MMADA is comparable to or even better than specialized models, highlighting the potential of diffusion models as next- gext-eennration foundation paradigm for multimodal intelligence. For future work, we aim to improve this new multimodal foundational framework and model from two perspectives: (i) we will try to scale both pre-training and post- training setting for more extensive applications; (ii) we will utilize diffusion-centric reinforcement learning al algorithms (e.g., TraceRI. (65) to enhance the capabilities of multimodal large diffusion language models.
10

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[81] Yang, Z. Yu, Meng, M. Xu. Ermon, and Bin, "Mastering text-to-imane diffusion: Recaptioning. planning, and generating with multimodal Ims."' in Forty-first International Conference on Machine Learning, 2024.
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[83] J. Austin, D. D. Johnson, J. Ho, D. Tarlow, and R. Van Den Berg, "Structured denoising diffiusion models in discrete state-spaces," NeurlPS, pp. 17981-17993, 2021.
[84] S. Gu. D. Chen, J. Bao, F. Wen, B. Zhang. D. Chen, L. Yuan, and B. Guo, "Vector quantized diffusion model for text-to-imant -maee synthesis." in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.
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A More Related Works
Multimodal Large Language Models for Multimodal Understanding Recent developments in large language models (LLMs) such Gemini-2.0 [66], ol -preview 667 and DeepSeek-R1 [68] have improved the evolution of multimodal large language models (MLLMs) 69 [1 Early efforts in this domain, including LLaVA (72), MiniGPT-4 773,, and InstructBLIP 39, showcased impressive capabilities in multimodal understanding. These studies advanced the integration of LLMs into multimodal contexts by projecting features from pre-trained modality-specifie encoders, such CLIIP [56]. into the input space of L.MMs, thereby facilitating multimodal understanding and reasoning within a unified transformer. Many efforts have been made for MLLMs regarding vision encoders, alignment adapters, and curated datasets [74, 75 40 76), and most of them follow an autoregressive generation paradigm that has been proven effective for text generation in LLMs. However, they are usually not capable of performing textual and multimodal reasoning concurrently. In this work, MMADA develops diffusion foundation models fill this gap.
Diffusion Models and Autoregressive Models for Visual Generation A large number of dif- fusion models [60| 77 61 62] 788-22 have demonstrated notable success in visual generation. In addition to the typical denoising diffusion process on the continuous space, series of frameworks, such as D3PM [83] and VQ-Diffusion [84), adopt discrete diffusion modeling [83] 85 for visual generation. Specifically, the image is denoted as a sequence of discrete tokens using pretrained image tokenizers [86 87 [24 [88] In ttee raining stage, the model is optimized to recover the original values
of a portion of these tokens that are randomly masked. Transformer series 89
2
91 has
demonstrated significant capabilities in autoregressive modeling for NI.P tasks. Many approaches [92]
96 try to apply the autoregressive modeling to perform visual generation by modeling semantic dependency within visual details. For example, LlamaGen (63] employs Llama architectures [91] and refines codebook design to enhance the performance of discrete tokenizers class-conditional image generation. VAR [31] replaces the "next-token prediction" paradigm with "next-scale prediction" by designing a multi-scale image tokenizer. However, existing autoregressive methods still lag behind diffusion methods in terms of visual generation capabilities. In this work, MMADA train diffusion models to model textual and visual contents, and infer efficiently with AR or Semi-AR sampling.
B Preliminaries of Discrete Diffusion, PPO and GRPO
B.1 Discrete Diffusion and Mask Token Prediction
Discrete denoising diffusion models have emerged as a powerful paradigm for modeling discrete data representations, particularly in visual and textual domains. Recent works such as VQ-Diffusion 197), MaskGIT (98), and Muse [99] demonstrate their effectiveness in generating high-quality discrete tokens. Building these advancements, Show-o [20] unifies discrete diffusion and mask token prediction into a single framework, enabling joint optimization of noise corruption and semantic recovery. Below, we formalize the key components of this discrete diffusion process.
B.1.1 Forward and Reverse Diffusion Processes
The forward diffusion process progressively corrupts the initial data wo (e.g., sequence of visu- al/textual tokens) into noisy latent variables æ1, æ1,.., Dr via a fixed Markov chain ((x; (.,1)) At each step, this process stochastically perturbs tokens-such or introducing special MASK] token. The reverse process, parameterized by a learned model pe. reconstructs wo from æт by sequentially sampling ((2,-112,,,0))
replacing them with uniform noise
To formalize this, consider a single token z at position i in Dg. where z/ E 21, K} corresponds to entry in codebook. For simplicity, transition probabilities between consecutive tokens are defined by a matrix Q2 e R with [Q:) q(2, m|æ n). The forward Markov process for the entire token sequence is then expressed as:
omit the index i in subsequent derivations. The
(( = T T (x))))(((111)
where m(r) is a one-hot vector of length K (with at position æ)) The categorical distribution over Iy is derived from (x)(x.)).
16

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A critical property of the Markov chain allows us to marginalize intermediate steps and compute the direct transition from 2g to Zyz (<)e v' (xx))"((1)a)))) (z=)Q, with Q, Q----Q-Q-----
(8)
Furthermore, the posterior distribution conditioned on zu is analytically tractable:
(v (0 (1))Q; (v (2
0(1=-1/04, zo) ((
v' (1,)Q, (
This property essential for deriving the variational lower bound of the diffusion process.
B.1.2 Transition Matrix Design and Masking Strategy
The design of the transition matrix Q: is pivotal to the success of discrete diffusion models. Early works [00) 101 propose injecting uniform noise into the categorical distribution, leading to:
Oi +
BB
B
8
B
at
+
3
3
Q:
=
(10)
B
B
04 8r_
where E [0, 1] controls the retention probability, and 3, = (1 - (1-a))/K ensures uniform diffusion all K categories. However, this approach often introduces abrupt semantic changes due to aggressive replacement of tokens with unrelated categories.
To address this limitation, Show-o adopts "mmask-andrreplace" strategy inspired by masked language modeling. A special [MASK] token introduced, expanding the token space to + states. The transition matrix redefined as:
Ta Be
3
8t
3
a1 +
B
3e
Q:
(11)
B
3r
+ Be
Y
Y
where: OOdinary tokens have a probability o₁ remain unchanged, be uniformly diffused,
and 7/t to be replaced by [MASK].- - The [MASK] token retains its state with probability 1.
This design explicitly signals corrupted positions during the forward process, enabling the reverse network to focus on reconstructing masked regions. The parameters satisfy a₁ + K K3, + % = ensuring proper normalization.
B.1.3 Variational Objective and Loss Simplification
Following the D3PM framework [001, the variational lower bound for the discrete diffusion process derived as:
T
[lg po 2 CELBO
E |loo (log + C.
(12)
To simplify training, Show-o leverages the mask token prediction objective. Specifically, the model learns a neural network pe to reconstruct masked tokens in X from the noised x,, following the methodology of MaskGIT 198). This approach avoids explicit modeling of the full posterior and instead focuses on recovering only the corrupted regions, significantly reducing computational complexity.
B.2 Proximal Policy Optimization and Group Relative Policy Optimization
Proximal Policy Optimization (PPO) 102 a widely adopted reinforcement learning algorithm that balances policy updates with stability through clipped surrogate objective. The core idea of PPO lies
17

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in constraining policy changes to a proximal region around the previous policy, thereby preventing large, destabilizing updates. This is achieved by introducing a clipping mechanism that bounds the importance sampling ratio during optimization. Specifically, PPO maximizes the following objective:
JPPo (0) (.gaa)) (-19)
(or 9 9,02) As, clip Team (o q, 0c1)
To(o: q. 0ct) Te (o; q, (.1)))
)aa)
(13)
- A.
min
where (g, a) denotes question-answer pair sampled from the dataset D, is the clipping threshold, and A, is the estimated advantage at time step t. The advantage Ag is typically computed using Generalized Advantage Estimation (GAE) [103], which combines multiple temporal differences with discounting and mixing parameters A):
A ACAE(T.))
Σ (72)) d with 6, R4 + 7V(441) V(s)..
(14)
This formulation ensures robustness against high-variance estimates while maintaining compatibility with off-policy data.
In contrast, Group Relative Policy Optimization (GRPO) 33 introduces two key innovations to address limitations in PPO's value function dependency and global advantage estimation. First, GRPO eliminates the explicit modeling of the value function (s)) and instead computes advantages in a group-relative manner. For each (g, a) pair, the behavior policy T generates G responses { and the advantage of each response normalized relative to its group:
A Vi mean [R (()))
(15)
std ( R, )2
where r; represents the raw reward for response o;, This design enables GRPO to focus on relative performance within local context, reducing sensitivity to absolute reward scales.
Second, GRPO extends PPO's elipped objective by incorporating an explicit KL divergence penalty term between the current policy my and reference policy Tee. The final objective defined as:
TGRPO (0) = (...))->.
G
lod
Σ
min (re,(0)ALe, clip clip (i)()r.()),11 1,.1--,,10 s,1+e +e) A)..) -BDKL (Tee [Te]||Tet)
0
(16)
Te (a. (9..0..))). is the importance sampling ratio time t. Notably, GRPO computes
where (0) New [o (q...c)..c the loss at the sample level: for each sequence, the per-token loss is averaged first, followed by averaging across all sequences in the group. This hierarchical aggregation enhances stability in multi-response settings.
C Sampling Details
Text Generation For text generation, we adopt the semi-autoregressive denoising strategy intro- duced in LLaDA (22, which integrates autoregressive decoding with diffusion--aase denoising. Specifically, the output sequence is partitioned into multiple blocks and generated from left to right. Within each block, logits are computed for all masked positions, and a subset of tokens is selected-either randomly or based on confidence scores-for denoising. The masking schedule follows linear schedule, consistent with LLaDA. The denoising process is repeated for given steps.
In our evaluation, we set the total sequence length N 1024 and perform steps. The sequence is divided into blocks of 64 tokens. At each step, we unmask the 2 tokens with the lowest confidence within the current block, irrespective of their positions. Once tokens in a block are denoised, the process proceeds to the next block. A qualitative comparison is provided below. As shown, a eemi-eutoreeressive denoising strategy tends to generate more intricate and detailed descriptions, whereas non-autoregressive fixed-length generation often produces very short responses.
512 denoising
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This observation is consistent with findings reported for LLaDA. For instruction-tuned models, given that the training process incorporates a substantial number of IEOSI tokens, directly applying the lowest-confidence remasking strategy without dividing into blocks leads to an unnaturally high frequency IEOSI tokens in the generated sentences.
Qualitative Comparison of Different Sampling strategy
Prompt: Please describe this image in detail.
Fix-length Generation Length 1024, Steps 512, Block Size 1024)
The image detailed painting depicting still life scene.
Semi-Autoregrresive <Length= 1024, Steps 512, Block Size = 64)
The painting depicts serene still life scene tablecloth with textured. multicolored pattern. In the center, there is rustic, cream-colored ceramic pitcher with brown accents, holding vibrant bouquet of sunflowers. The sunflowers, with their bright yellow petals and deep brown centers, arranged a way that they seem to burst forth from the pitcher, creating a sense abundance and vitality. To the right the pitcher, there is a small, ornate glass vase with a patina finish, adding an element of contrast and visual interest to the composition. In the foreground, sliced loaf of bread is placed on the tablecloth, with piece of bread and knife nearby, suggesting meal or snack. The background softly blurred, focusing attention on the main elements the painting. The overall composition balanced and harmonious, with the warm tones of the sunflowers and ceramic pitcher complementing the cooler bronze of the vase and the soft pastels of the bread.
Image Generation For image generation, we adopt a low-confidence remasking strategy and follow cosine noise schedule, consistent with the setup in Mag VIT v2. In contrast text generation, do not employ a semi-autoregrresivee approach; instead, the entire output sequence treated as single generation block. During evaluation, we generate sequences of length 1024, corresponding to 5 x522 resolution images. The denoising process consists of 50 timesteps, and we apply classifier-free guidance with guidance scale 3.5.
D Details of UniGRPO
In this section, detail our UniGRPO training process and illustrate the differences compared to LLaDA and di (diff-GRPO) [34]. Notably, LLaDA does not incorporate a reinforcement learning procedure, yet it includes algorithm for computing log probabilities. Diff-GRPO introduced the first GRPO-style RL algorithm for diffusion language models and proposed an alternative method to compute log probabilities for given question-answer pair. We first outline the methodologies of these two prior works:
LL)AA While LLaDA does not employ an RL process, it offers method to estimate the log probability of given pair (g. a) using Monte Carlo simulation. For a given answer, N mask ratios are randomly sampled from the interval (0. 1) (where N = 128 in the original work). Subsequently, a batch of N inputs is constructed. Each input instance consists of original question tokens concatenated with the answer, where portion of the answer tokens (corresponding to the sampled mask ratio) replaced by [MASK] tokens. model forward pass performed on this batch, and the logits corresponding to the masked regions are collected and averaged obtain the log probability
19

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Algorithm UniGRPO Policy Gradient Optimization
Require: Reference model Tief, prompt distribution D, number of completions per prompt G,
number of inner updates μ., diffusion steps T
1: Initialize policy <-
2: while not converged do
3: Told < TT
4: Sample a prompt q ~
5: Sample G completions ~ [. 9), for i E [G]
6:
For each o;, compute reward r; and advantage Af (TTi)) using Eq. Sample starting timestep 40 M(0
15
7:
8:
Generate μ 1 uniformly spaced timesteps t1, - from foo
9:
for gradient update iterations n L..... do
10:
if n 1 then
11:
Sample starting mask ratio r1 ~ (0, 1) and compute initial timestep 1 = [nx -T)
12:
else
Uniformly divide remaining timesteps: i, ( (T (T-4) &1
14:
Construct input (g. masked o;) using timestep in (with always unmasked) For TT, Told, Tyt. estimate log-pro peg-rbbabilities of masked tokens in o at t,,
15:
16:
Compute UniGRPO objective Eq. (5) and update Ty via gradient descent
17:
return T0
logp(g, a). The principal drawback of this method is its significant computational overhead, rendering impractical for on-policy RL algorithms.
(2) d1 (diP--GRPO) The dI framework (34] introduced the first GRPO-style RL algorithm for diffusion language models. To overcome the efficiency issues of LLaDA, di employs novel masking strategy. In each iteration, only single forward pass is performed, eschewing Monte Carlo simulation. The input is constructed by applying a random mask to the question tokens and completely masking all answer tokens. The stochasticity is intended be achieved by selecting different tokens for masking across different iterations, even with the same mask ratio. While this approach in d1 enables GRPO training, we identify potential limitations:
Question Masking:
The random masking of question tokens is of unclear practical sig- nificance. In typical question-answering scenarios, the question is always fully observed during both training and inference. We argue that such random masking of questions serves primarily to introduce stochasticity without direct relevance to the task's practical application.
Answer Masking Strategy: By consistently masking the entire answer, the model is effectively trained only on the initial denoising step (i.e., predicting the full sequence from a fully masked state). We contend that this approach provides insufficient learning depth, potentially causing the model to behave more like single-step predictive AR model rather than leveraging the multi-step denoising capabilities inherent to diffusion models. This underutilizes the diffusion model's 's unique strengths.
As introduced in Section 3.3.1 our UniGRPO addresses the masking issues with the following key modifications:
1. Unmasked Questions: In UniGRPO, all question tokens remain unmasked during training. This aligns the training process with the inference conditions and practical use-cases where the question is always fully provided.
2. Iteratively Varied Answer Masking: We apply a random mask ratio to the answer tokens. Crucially, this mask ratio, denoted Pa. varies uniformly with the training iteration (e.g..
Pa (0, 1) sampled anew or cycled through discrete steps each iteration). This strategy aims to preserve stochasticity while ensuring the model is exposed to various stages of the diffusion denoising process, from nearly fully masked to nearly fully denoised answers. By doing so, UniGRPO learns from multi-step denoising information, which is consistent with
20

---

conventional training methodologies for diffusion models and allows for the full utilization of their multi-step generative power.
Through this design, UniGRPO captures the essential multi-step denoising dynamics of diffusion models. By allowing the model to predict answers under diverse masking conditions while preserving the natural structure of the imput, it avoids the pitfalls of both computational inefficiency (as in LLaDA) and oversimplified prediction (as in d1). The training procedure of UniGRPO is outlined in Algorithm At each iteration, we sample mask ratio rи uniformly from predefined range, apply this ratio to the answer tokens, and perform a single forward pass using the unmasked question and masked answer. We then compute loken-level log-likee ogeliiils in the masked regions, apply the GRPO objective, and update the policy accordingly.
E
Ablation Studies
E.1
General Ablations on Mixed Long-CoT Fine-tuning and UniGRPO
We present quantitative ablation results of our MMADA across different training stages: Mixed Long- CoT fine-tuning and UniGRPO. All results generated follow the sampling process in Appendix C As shown in the Table after Stage 1, our model still lags behind most baselines. In Stage 2, Mixed Long-CoT finettunngg substantially enhances the model's reasoning capabilities, particularly in mathematical and geometric domains. In Stage 3, UniGRPO further improves performance, allowing the model to achieve results comparable to state-of-the-ar methods across various tasks, including mathemaane mathematical reasoning, geometric problem-solving, and image generation benchmarks such as CLIP Score and Rewae These results demonstrate that UniGRPO effectively boosts both the model's 's understanding/reasonigg and generative capabilities.
Table Ablations on Mixed Long-CoT fine- lin-ttnning and UniGRPO
Model
GSMSK
MATH500
GeoQA
CLEVR
CLIP Score
ImageReward
MMADA After Stage
17.4
4.2
8.3
23.1
0.69
Mixed Long-Co'l' Finetuning
65.2
26.5
15.9
27.5
29.4
0.84
UniGRPO (MMADA)
73.4
36.0
21.0
34.5
32,5
1.15
2.5
UniGRPO diff-GRPO
Correctness Reward
2.0
1.5
1.0
0.5
1000
2000
3000
4000
5000
RL Steps
Figure 6: Comparison of different masking strategies GSM8K reward trends during training.
F.2 Design Choices of UniGRPO
Effect of General Masking Strategy To evaluate the impact of our proposed masking strategy, we first conduct comparative analysis with d1 34 within our reinforcement learning framework. Given the substantial computational cost associated with large-scale ablation studies, we perform
21

---

these experiments on the GSM8K dataset, utilizing 8 A100 GPUs, Both the original di methodology and our UniGRPO approach are applied to this dataset, starting from the same pre-trained checkpoint of our MMADA. We present the reward trends during training in Fig. 6
As shown in the figure, our method consistently achieves higher reward values during training. aligning well with our theoretical analysis. In contrast to d1, UniGRPO removes masking from the question and applies partial masking to the answer rather than masking it entirely. This results in input sequences that retain partial noise, encouraging the model to learn across multiple denoising timesteps. Consequently, this better leverages the intrinsic characteristics of diffusion models and improves the overall learning capacity.
Effect of Uniformly Random masking In place of fully random masking across iterations, adopt a uniformly random masking strategy for the answer portion. Specifically, we first sample a random starting timestep, and then uniformly generate the remaining denoising timesteps across the full diffusion timesteps (set to 1000 our experiments). For instance, given a randomly selected starting timestep of 100 and total of training iterations, the remaining timesteps uniformly spaced and set to 300, 500, 700, and 900. This design ensures a more consistent coverage of the diffusion process while retaining randomness.
illustrate the training reward trends resulting from this structured masking strategy Fig. 7 As shown, the baseline approach with fully random timestep selection tends to introduce instability during training, leading to more frequent reward fluctuations and requiring greater number of steps to converge. In contrast, our uniformly spaced sampling strategy effectively approximates the behavior of Monte Carlo averaging in log-likelihooe estimation, resulting in improved stability and faster convergence.
2.5
Correctness Reward
2.0
1.5
1.0
0.5
Uniformly Random
Fully Random
1000
2000
3000
4000
5000
RL Steps
Figure Comparison of different random masking strategies on GSM8K reward trends during training.
22

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Qualitative Comparisons (with Reasoning CoT)
Qualitative Comparison of World Knowledge-AWee Aware -omaaoe-mmaee Generation (1)
Prompt:
The largest terrestrial carnivore from the Arctic.
Other models:
Show-o
Emu3
Janus Pro 7B
MMADA:
<think>
largast terreatrial from the Arctic the Polar Bear
</think>
majestic polar anovy landscape, gloving against icy backdrop.
Qualitative Comparison of World Knowledge-Awar Texl-to-Image Generation (2)
Prompt: famous landmark building in Europe.
Other models: Show-o
Emu3
Janus Pro 7B
MMaDA:
<think>
landnark building in Europe likely Eiffel
</thinic>
Eiffel hollow, cloudy sky.
23

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Qualitative Comparison of World Knowledge-Awee Aware Text-to- Text-to-Image Generation (3)
Prompt:
A traditional strong spirit from Russia.
Other models: Show-o
Emu3
Janus Pro
MMaDA:
The user is dascribing traditional strong spirit from Russia. À vell-kmovn spirit that fita this description 1s vodka.
</think>
silver bottle of vodka sits next to decanter and halved line.
CAMEINS
Qualitative Comparison of World Knowledge- Text- to- Image Generation (4)
Prompt: A famous statue gifted to the United States France.
Other models: Show-o
Emu3
Janus Pro 7B
MMADA:
<think>
The description rafers vell-knesm landnark that fron France United States.
This is known as the Statue of Liberty.
</think>
A lou-angle shot of the Statue of Liberty against blue with feu clouds.
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Qualitative Comparison of Textual Reasoning (1)
How many plums will balance one pear, given that 3 apples and one pear weigh as much as 10 plums, and one apple and 6 plums balance one pear? Assume that fruits of the same kind have the same weight.
Question:
Show-o:
< Show-o does not support this task
Emu3:
< Emu3 does not support this task
Janus Pro 7B:
Jamus Pro does support this task
MMaDA:
<think>
nany pluns pear. The problen given some information. Let try to parse it step step. plums. Then,
Okay, see.
figure
balance seight single
First, the problen aays: applea together with pear have the aane weight need find out hov many
the combination of one apple and pluns weighe the same pluns are needed to equal the veight of a single pear. Since all fruits are equal in veight, probably the
Let ne start by writing this down. Let's assign variables. Let's let A represent the reight as eeight plun. So, based on
waight pear, stazament given:
apple, It
10M
Right, because 3 apples Second, saying one apple and 6 pluns weigh the same as a pear. So, That's bacause all apples pluns equal. A =- GN
pear equale 10 plune.
So, nou have equations:
1) 34 = 10M
6M R
find how many pluns needed balance pear. That need find the value n in nM.
Our goal
From equation (2): 6M R
GM-
Now aubstitute this value of A into equation (1): 3(R 6N)
veighs much aa 7 plune.
Let's check with equation (2):
eN But if == 7M, then A 7M 6MM M. plun, which works when check the first equation: 3A 10M eatiefied.
6X
apple veighs much 3(1) + 7M 10N 3M 7M 10N
equations
Therefore, the ansver is 7 plume. </think>
To determine how many pluns are to balance single pear, we set up the equations based on the conditions:
1. Let A represent the veight of an apple, R represent the waight of pear, and the waight plum.
2. The equations are:
Equation 1: + == 10M
Equetion 2: A 6M (1 apple plune equals pear)
*+Step 1: Solve for in terns It and M+* From Equation
A into Equation 1sa
6M
**Step 2: Subatitute
3 6M)
10M
3Rt 18M + ft 10M
18
10M
4R 28M
7M
*4Final Anaver:**
pluna are balance veight of single pear.
25

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Qualitative Comparison of Textual Reasoning (2)
Question: How many positive divisors does the integer possess, where =7 11 13
Show-o:
< Show-o does not support this task
Emu3:
< Emu3 does not support this task
Janus Pro 7B:
Janus Pro 7B does support this task
MMaDA:
<think>
integer has, 7 cubed tines 11 equared tines 13 the fourth power. Ban, let think. I remenber that the number of divisore of number ie related to its prine factorization exponente. Let ne try to recall the general formll
Okay, need figure
many positive
given as
First, right that if nunber is axpressed in a product prines, like number in the forn n pf x P. x ... x p then the number positive divisors is (a 1b 1e....e 1). Right? Because for each exponent in the prime factorization, you add then multiply then all together for the total numbar of divisors. verify that,
positive divisors are 1, Using the formula, (1+1)=(1+1) 2+2 = shich checks out. Anothar example: like 2 3 Then the divisors are 1, 2, 4, 3, 6, That nakea divisors. The formula gives (2+1)+(1+1) 3+2 that's correct. Another example: 2 x 5 Then divisors count is (2+1)+(2+1) S+3-9. So the formula seens correct.
example:
2 3
So, applying
the problem: 3, 2, and 4. 8o, applying the number of divisors formula should be (3 + 1) 14 + 1). Lat's compute
formila
7 x 11 x 13*
the exponents
step by step.
3. 4 + 1a Then, miltiplying those recults together: & 5. Let ne check that. tines 3 is 12, then 12 times 60. So the number positive divisors 60.
First, calculate each exponent plus one: 3+1 is 4. 2
Wait, but let ne make sure didn't make mistake in the mitiplication. Let no varify the steps Yes, that But juat to be thorough, let ne write the original number 73 11 x 134, The prine factors they are prine,
again. 443 is
Each raised the formila for divisors usea the exponenta factors plus Then miltiplying the results: 4+345-60,
exponents 2,
reepectively.
this could be wrong? Vell, naybe the exponents different, but the original expression is already in its axponential form. Let ne check if migread. For example,
Is there any possible
the exponenta
problen writes 7 times 11 times
13 sо the exponents are 3, 2, 4, that's correct. Alternatively, could
miltiplication?
me
again. 4+3
12, 12+5 60. Correct. 30 is the correct ansver.
Wait, just to be thorough, let's list the combinations: any divisor is 7" x 11 13 where S 2, and 4*3+8 60 peasible positiva diviaors. Parfect.
S
4. The nunber of choices for 4, for 3, for is
don't any </think>
**Solution:es
The integer is given as 7 11 184, To determine the number positive divisors of na
use the formula for the nunber divisors: if nunber in the form pi P1 x Pk then it has (e+ 1) 1)... (c+ divisors. +eStep-by-Stop Solution:**
*=Identify the Expanent:*x
73, Exponent 3
111; Exponent
13": Exponent 4
++Appl the Divisor Formula:*s
For each prine
+ 1), and then multiply:
For exponent 3: 3+11 =4
For exponent 2+1=3
For expocent 4: 1+1=3
Multiply the
4x3x3=
*nsver::* The integer n has 60 positive divisore.
26

---

Qualitative Comparison of Textual Reasoning (3)
Question: A "Hope Number" a natural number with an odd number of positive divisors. Find the largest "Hope Number" within 1000.
Show-o:
< Show-o does not support this task >
Emu3:
Emu3 does not support this task
Janus Pro 7B:
< Jamus Pro 7B does not support this task
MMaDA:
<think>
Okay, I need find the largest "Hope Nunber' shich natural nunber with odd nunber of positive divisors. Let ne start by recalling shat a Hope llmbber is. The question says number has
number divisors. Run, that Firet, remember that nunbers have divisors related to their prine factorization. If number has the prine factorization
number
different divisors.
"1 P2" ...Pk". Pk" 1 is odd, i.e., every exponent is even. That perfect square (since each exponent even).
nunber of divisora (ai [)((2 +)) (a)=1).
This product is odd and only if every means the number itself
factorization is 3s. Its divisors three divisors, which ia odd. Anather example, 16. Prime factorization is 24, Ita divinors are prine factorization
check: for example,
Its
9. That's
divisore, which odd. 3 x 5, shich is not perfect square. Its divisors are 1, 3, 5, 15-that's 's four divisors, shich is even. Therefore, only perfect squares have an nunber of diviaors.
2,
That's
Therefore, all Hope Numbere lees than 1000 perfect Therefore, the problen reduces to finding the largest perfect equare less than or equal to 1000. Let's compute that.
The aquare reat of 1000 is approximataly 31.62. So ve need to check the largeat integer n auch n² < 31 = 961. 322 = 1024, which is greater than 1000.
= is the largest perfect aquare within 1000. Therefore, 961 the largest Hope Nunber within 1000.
But wait, let na confirm that 961 indeed a Hope Nunber. Lat's count its divisors. Its prine even, numbar of divisors
factorization 312, and 31 prina. exponenta 2+1= 3: 1, 31, 961, which is odd. That checks out. perfect equare
thorough, are none.
322 1024
Therafore, Largest Hope </thini>
largeat "Hope Number' within -SSeppby-SSte Explanation:**
**961*x.
*eUndorstandding Hope Property:** Hooe number has an add number of divisors *eif and only if it is perfact squarewx (because in the formila,
positive
exponenta must 2. *Larggsst Hope Number;* Compute parfect
a; odd, their product. add).
1000. 31 961 and 32 1024. Since
32 > 1000, the largest one is 31 1911.
**terification=* Any non-square nunber below has perfect equares have an odd number of divisore.
only
**Ansver:* 961
27

---

Qualitative Comparison of Textual Reasoning (4)
Question: What is the result of (20 (12+
Show-o:
Show-o does not support this task
Emu3:
< Emu3 does not support this task
Janus 7B:
< Janus Pro 7B does not support this task >
MMaDA:
<think>
Okay, let ue try to figure out the result of this expression: [20-16)X(1-4)) Ham, let's break this step by step. first.
nake sure apply the order operations correctly. Right, order of operations: parentheses first, then milsppicaation and division from left to right, then addition subtraction from left to right. Let ne write the expreaston again visualize it better: (20-16)x(1245)
So, the parentheses within the is 4. According to PENDAS, should handle the parentheses first before doing the multipiicationn division.
shich is part the fraction, and the denoninator
inside parentheaes, look at parentheses part: (20 16) should compute each part separately.
these
Starting with the first parentheses: 20 16. That's straightforvard. 20 ninus 16 equals Okey, that part simplifies Next, the second part inside the parentheses: 12 + 8. That's addition, so 12 plus in 20. So
the So now, the numerator is x 20. Let ne do that miltiplication. 4 tines 20 is 80. So the nonerator simplifies to
becones 20.
Nou the entire expression is 30 Dividing that number by Lat me check that division.
into twenty times (eince 20 ie 80), so 80 4 20. the reeult is 20,
Wait second, let verify that again make didn't make mistake. Let's go through each step again.
Original expression: (20- (20-16)x(1249)
parentheses:
4.
Second parenthesee: 12 + 20. Check.
Maltiply those tvo:
80. Check.
Divide by
right. But wait, nayba thare's another way to approach this. Somatines with order of operations, parenthezes are easier to catch. Let's see. The original expression is (20-16)x(12-5) numerator give multiplication,
4 20. Yop, that
You could The I Simplifying, 4 x 20 = 80, then 80 divided by 4 is 20. Alternativaly, you could factor out the 4 in the nunerator and cancel with the deneminator, but in this case, the result is still dona from left right.
parentheses
dononinator
20. that's goad way. Eithar vay, mittipiication and divisian Alternatively, let's check if there's de possibility that the entire numerator is being divided. divided by
so 4 that's 's another to see it, shich is reassuring. So that seens consistent. Another say to chack: expression is written with multiplication and division, the order of operations tella to do all cal calcutitions in the numarator first, then divida by the donominator. All steps lead to 20. divided
Let's see.
entire expression
80,
So
at it by using properties oparations. But
case, since
verify miltiplication. no errore in the operations. think
Another
So
ne just sure there's no alternative interpretatioon. Let's urite the original expression again:
solid.
should
Original expression: (30-16)((215))
The nunerator is 20 80, denoninator is 4, So 80 divided by 4 is 20. Yep, exactly, that's correct.
Therefore, I'm confident that the ansver is 20.
</thinic>
The result (20-16)x(1245) calculated follows: **Sinplify the parentheses inside the mu -20-16=4
2. **Multiply the simplified results:**
20 80
3. **Divide by 4:44
50 = 20
*+Pinal Ansvcr:**
20
28

---

Qualitative Comparison of Multimodal Reasoning (1)
P
B
Question: In the given figure, square ABCD is inseribed in circle with center O, Point P is located on CD. What is the value of angle APB?
Show-o:
The angle APB degress.
Emu3:
The question seems to be mix of letters and numbers, not coherent question or statement. If neant "is" "are", the correct statement would be "The neasure of angle APB 1s" "The statement
you angle APB 1s" or "The neasure of angle APB is".
angle
Janus Pro 7B:
find neasure angle APB, **Suue ABCDes; The equare is circunscribed around the circle, neaning all its sides tangent to the circle. This implies that the diagenal the square is equal to the dianeter of circle.
use the propertiea
square
circle.
2. **Cirele Dee: The center of the circle is also the center of the square. diagonals
>DDiagonals
Squaree+:
square
are equal
length.
**Angle APB*+: Since ABCD aquare, angle APB is right angle (90 degrees).
Therefore, the neasure of angle APB is 90 degrees.
MMaDA:
<think>
daterming the neasure angle APB, va start by noting that square ABCD is circunacribed within the
circle is circuncircle of the square, circle ie the same as the center of the square. Let's denote the center of the circle as 0. right
circle This
equal length bisect angles. This neans that angle A0B is 90 dagrees because the diagonals of square are equal and bisect each other at right angles.
square, the diagonals
consider triangle APB. Since lies the angle subtanded by chard at the center ia tvice the angle subtended at any point on the circunference
the property of the circle that
chords of circle meeting point P on CD (which is opposite AB). Points A and B are at adjacent vertices, and P is on CD. key point that when noves along CD, angle moves along the are oppasite AB. For square inscribed in circle, the are AB aubtenda right angle at any point on the circle opposite AB (i.e., on are CD). Thereforo, for any point on CD, angle APB alvays 45", This follown because /APB in half the degrae the which 90*, so:
this
fixed,
ZAPB
90" 45"
Thua, the neasure of angle APB ia </think>
45
The neseure of angle APB
29

---

Qualitative Comparison of Multimodal Reasoning (2)
D
E
F
C
In the diagram, triangle ABC is shown. Points D and E are located segments AB and AC, respectively. Lines and CD intersect at point F. Given that angle BAC 60", angle DCA is 40", and angle EBA is 30,, what is the measure of angle CFE?
Question:
Show-o:
angle measure angle 60".
Emu3:
solve for the
neasure of angle CFE, utilize the properties triangles and 30", angle
anglea. Since angle
40", angle
find angle ACD by aubtracting angle triangle ABC is an isoscelea triangle, angle BAC neasurea 40", Therefore, angle CFE can be found by cubtracting angle BAC from angle ABC: 40* 110* -30.. Hovever, angles cannot have nogative take positive value, giving 60*. Therefore, the degree 60"
angle ABE from 180"; 180* 40 30 110*, Since
of angle
Janus Pro 7B:
neasure angle First, ve know that angle ABE is 30*. Since angle ABE and angle ACD are adjacent. angles in the same triangle, they are aupplementary. Therefore, angle Next, know that angle is 60". Since angle and angle ACD adjacent angles the triangle, they are aupplamentary. Tharefore, angle is 180* 60* 120". Now,
properties triangles
given angles.
30 150*.
angle CFE. Angle CFE
supplement angle angle 180*
60*.
Therefore, the degree measure angle CFE is 60".
MMaDA:
To determine the nessure angle CFE, need to analyze the given information and the properties of triangles and angles. **Identify the given angles:**
Angla A 60*
Angle
Angle ABE 30
fact the aun of the angles in triangle 180" triengle, the of the angles is 180", Therefore, have:
60 LADC 40 180
60" LAEB 30" 180"
3. **Deternine the measure of angle CFE:** angle non-adjacenn interior angles. Tharetore, angle ABB angle ACD angle CFF 40* angle CFE 90"
angle triangle
equal
Therafore, CFE 90* 40* 60*.
Thus, the measure of angle CFE
angle
30

---

Qualitative Comparison of Multimodal Reasoning (3)
Question:
What number of large things are the same material as the purple cylinder?
Show-o:
There large things that the material the purple cylinder.
Emu3:
2
Janus Pro 7B:
There is one large object that shares the came naterial the purple cylinder, which is the large green cylinder.
Anewer: 1.
MMaDA:
<think>
determine the number items in the image, let's analyze the objects present:
The large green cylinder made of naterial that appears
rubber.
2. The large yellou cube is nade of naterial that appears to be netal.
euall grey
appears
4. The small cyan sphere is made of naterial that appears to be metal.
small cyan cylinder
appears
The amall purple cylinder is made of naterial that appears be rubber.
The green cylinder is the enly large object made of naterial that is the same as the snall purple cylindar. Therafore, there large object nada of the naterial purple cylinder.
</thinks>
The number of large things that the material purple cylinder
Qualitative Comparison of Multimodal Reasoning (4)
Question: Divide 24 apples equally among the people in the picture, how many apples can each person get?
Show-o:
In the image, there are four people, each with different number of apples. The with the apples has the apples.
Emu3:
24.
31

---

Janus Pro 7B:
divide apples equally anong the four people in the picture, you sould divide 24 by 4. 4
person can get apples.
MMaDA:
<think>
many apples person can get, divide apples by
the nunber of people.
24 + =4=6
each person get apples.
</think>
Each pereon can get 6 applee.
G Qualitative Examples (without Reasoning CoT)
MMADA can also efficiently handle general and simple tasks without generating CoT.
Qualitative Example of Text-toIImage Generation
32

---

Qualitative Example of Multimodal Understanding (1)
MMADA:
The image captures serene and picturesque seene of group dolphins swimming in ocean during sunset. sky painted with hues of orange, yellow, and pink, transitioning into deep blue as it meets the horizon. The sun is setting on the horizon, casting warm glow that reflects off the water's surface, creating shimmering effect. The dolphins are posi- tioned in the foreground. with their bodies partially submerged the water, their heads and upper bodies visible above the surface. The water appears calm, with gentle ripples around the dolphins, adding the tranquil atmosphere. The overall composition the image conveys a sense peace natural beauty.
Prompt: Please describe this image
in detail.
Qualitative Example of Multimodal Understanding (2)
MMADA:
The image is a vibrant and colorful landscape painting that captures the essence of sunset over field of yellow flowers. The sky stunning blend of warm, soft hues, transitioning from deep purples and blues the top warm oranges and yellows near the horizon. The sun is positioned low on the horizon, casting golden glow that illuminates the sky and the field below. The field of yellow flowers, likely sunflowers, stretches
the foreground, creating contrasts beautifully with conler tones sky. The paint- ing is executed with broad, expressive brushstrokes, giving dynamic and lively feel. The overall composition is balanced and harmonious, capturing the tranquility and beauty of the natural world.
of color that
Prompt: Please describe this image
detail.
Qualitative Example of Multimodal Understanding (3)
MMADA:
The image depicts modern dining with minimalist aesthetic. The centerpiece is long dining table with sleek, dark finish, supported by four sturdy legs. The table is set with a few items, including black vase, few books, and small decorative object, adding touch of elegance to the space. The table surrounded by four blue velvet chairs with wooden legs. providing a striking contrast to the dark tones the table, The room has clean, open feel with light-colored walls and large windows that allow natural light flood in, creating bright and airy atmosphere. The left is round mirror with simple frame, enhancing the modern look the room.
Prompt: Please describe this image
in detail.
33

---

Qualitative Example of Multimodal Understanding (4)
MMADA:
The image depicts a charming, narrow street in European city, likely in, given the architectural style and the presence of a red tram painted in soft pastel colors, including shades pink, green, and beige. The buildings have ornate balconies and shutters, adding the quaint charm of the scene. The street is paved with cobblestones, and there tram tracks down the center, indicating that this tram lane. red tram is parked on the right. The tram is stationary, and the overall atmosphere serene and picturesque.
The street lined with tall, narrow bui buildings
Prompt: Please descrihe this image
detail.
34

---

NeurIPS Paper Checklist
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36

---

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37

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38

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Reviews:
[{"review_id": "nyTY8u5xGO", "reviewer": "Reviewer_gskN", "summary": "This 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.", "questions": "Given 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.", "limitations": "yes", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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?", "was_revised": true, "final_justification": "The paper received a high score from me, and I’m keeping it as is.", "rebuttal": "We sincerely thank you for your thorough evaluation and constructive feedback. We are grateful for your recognizing the technical soundness of our UniGRPO method, the motivation of our unified discrete formulation across modalities, the strength of our empirical results, and the significance of our research direction. We address the specific comments and questions below.\n\n**Q1:Lack of comprehensive ablation studies**\n\nA1: We thank the reviewer for this insightful suggestion. We agree that a comprehensive ablation study is crucial for understanding the individual contributions of our key components. However, conducting a full factorial ablation on an 8B-parameter model is computationally prohibitive. Within the limited time of the rebuttal period, we have performed as many ablations as possible and report preliminary results to isolate the contributions of each major component under constrained training budgets.\n\n1. **Ablations of the part of the unified architecture.**  \nTo examine the core contribution of MMaDA’s unified probabilistic formulation based on discrete diffusion, we conducted a comparison experiment with an autoregressive (AR) baseline using LLaMA-3 8B as the backbone. **The only difference lies in the training loss and inference method: the AR baseline uses AR-style likelihood loss and greedy sampling, while MMaDA employs diffusion-based objectives and decoding.** This baseline resembles Emu3 [1], an AR-based unified multimodal model.\nBoth models were trained under identical settings (data, compute, batch size) for **100K** steps using 64 A100 GPUs. For both pre-trained models, we evaluate on foundational generative tasks: conditional text generation using Perplexity (PPL), image captioning using CLIP Score, and class-conditioned image generation using FID on ImageNet. We also report image generation latency to assess practical efficiency. The results are reported as follows:  \n**Table a: Comparison with AR architectures**\n| Model                     | PPL ↓ | Image-Caption CLIP Score ↑ | FID ↓   | Image Generation Latency↓ |\n| ------------------------- | ---- | -------------------------- | ------- |-|\n| AR (LLaMA-3 8B, AR loss)  | **23.1** | 14.5                       | 16.2    |~120s|\n| MMaDA (Unified Diffusion) | 25.3 | **16.9**                   | **12.4** | **~7s**|\n\n    These results show that, under matched conditions, MMaDA **outperforms the AR-based baseline on image generation and multimodal understanding tasks**, while maintaining competitive performance on language modeling (PPL). Most notably, MMaDA achieves a **17× speedup in image generation latency**, highlighting the efficiency advantage of diffusion-based decoding. The improvements stem from the structural benefits of diffusion models for vision tasks: diffusion avoids the **unnatural raster-order decoding and error accumulation** observed in AR models, which results in both lower fidelity and substantially higher latency. While the AR baseline achieves slightly better PPL, we attribute this to the relatively underexplored decoding strategies for diffusion-based language generation.\n    To further validate this conclusion, we note that a recent work, **UniDisc [2]**, independently explores unified modeling using discrete diffusion and compares it with autoregressive (AR) approaches on joint text-image generation and inpainting tasks. Their findings are consistent with ours: discrete diffusion models offer **substantial advantages in image generation**, while achieving **comparable performance in text generation**. These aligned observations across independent studies strengthen our claim that discrete diffusion serves as a more natural and effective generative mechanism for multimodal modeling compared to AR-based approaches.\n\n2. **Ablations on mixed long-CoT fine-tuning, and the UniGRPO**  \nFor these two parts of MMaDA, we included our evaluation results of MMaDA across stages in **Appendix E.1**. As shown in Table 6, we report model performance after each training stage across a range of reasoning and generation benchmarks. After Mixed Long-CoT fine-tuning, the model shows substantial gains in tasks requiring multi-step reasoning, particularly in mathematics (GSM8K: +47.8, MATH500: +22.3) and geometric understanding. UniGRPO further boosts performance across all benchmarks. These results clearly demonstrate the complementary benefits of each component: Mixed Long-CoT fine-tuning primarily enhances reasoning abilities through multi-hop instruction tuning, while UniGRPO improves both understanding and generation through reinforcement-based optimization. Together, they enable our model to match or exceed the performance of strong task-specific baselines in a unified manner.\n\n\n**Q2: Scalabilities of our model?**\n\nA2: The principle that performance scales with model size is well-documented for autoregressive LLMs. Encouragingly, this trend appears to hold for diffusion-based and unified models as well. The performance leap from MDLM [3] to LLaDA [4] in diffusion LLMs, and the significant gains observed when scaling from Janus-1.5B [5] to Janus-pro-7B [6] in unified MLLMs, strongly suggest that our framework would similarly benefit from larger model sizes.\n\n**Q3: Fundamental barriers of scaling?**\n\nA3: The primary barrier to scaling at present is not architectural but rather the availability of **powerful, open-source base diffusion language models.** Our unified probabilistic formulation requires a diffusion-based text model as its foundation. The current open-source ecosystem is largely limited to models in the ~8B parameter range (e.g., LLaDA-8B [4], Dream-7B [7]). The future development of larger and more capable base diffusion LLMs would directly enable us to scale our training paradigm and likely unlock substantial further performance gains.\n\n**Q4: Conditions for Poor Performance?**\n\nA4: Our model currently faces challenges in tasks that demand extremely **fine-grained visual understanding**, as well as OCR tasks.\nThis limitation stems from two main factors related to our vision encoder. We use a **MAGViT-v2-based VQ encoder.** While effective for general scene comprehension, its reconstructive fidelity for intricate patterns like text is limited. The publicly available weights are not sufficiently trained for these specialized, fine-grained tasks.\n\nBesides, the visual encoder was primarily trained on a reconstruction objective. This is a gap compared to state-of-the-art approaches that use vision encoders like SigLIP, which are pre-trained on massive datasets for deep semantic understanding. \nWe identify this as a key area for future work. In the future, we plan to integrate a more powerful and **unified tokenizer**, such as UniTok[8], into our framework.  We believe this will significantly enhance the model's perceptual capabilities.\n\n[1] Emu3: Next-token prediction is all you need  \n[2] Unified multimodal discrete diffusion  \n[3] Simple and effective masked diffusion language models. Neurips 2024   \n[4] Large language diffusion models.  \n[5] Janus: Decoupling visual encoding for unified multimodal understanding and generation. CVPR 2025.    \n[6] Janus-pro: Unified multimodal understanding and generation with data and model scaling.     \n[7] Dream-7B.   \n[8] Unitok: A unified tokenizer for visual generation and understanding."}, {"review_id": "GwKiSDTTVr", "reviewer": "Reviewer_Xen8", "summary": "The 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.", "questions": "* 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?", "limitations": "Please refer to the weaknesses.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# 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.", "was_revised": true, "final_justification": "The 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.\n\nAfter the rebuttal, the authors have addressed my concerns successfully, and I give my final score as accept.", "rebuttal": "We sincerely thank you for your time and efforts in reviewing our paper. We are glad to see your acknowledgement of our model's single framework across modalities, state-of-the-art unified performance, and the clear clarification of our three stages of training and thorough ablations. We provide responses for your concerns below:\n\n**Q1: Comprehensive ablation studies of UniGRPO process.**\n\nA1: Thank you for the insightful suggestion. We agree that a more fine-grained ablation of the UniGRPO components is important for understanding their individual contributions and interactions. While our primary contribution lies in the unified diffusion framework and three-stage training, UniGRPO is implemented with **minimal deviations from the original GRPO to ensure reproducibility.** We retain KL regularization, use a uniform-ratio mask scheduler, and adopt equal reward weights across modalities for clarity.  \nIn response to your suggestion, we have conducted a more detailed ablation study starting from our stage-2 checkpoint. We independently modified each of the following components: (1) removing the KL constraint, (2) replacing uniform-ratio masking with random masking, and (3) varying the weighting of the reward heads across modalities. Each variant was trained for 4,000 steps on 64 A100 GPUs, and we report the performance on the representative benchmarks for three tasks in the table below:\n\n**Table a: Ablations on Components of UniGRPO.**\n|Setting|KL|Mask|Reward Weighting|GSM8K|GeoQA|Image Reward|\n|-|-|-|-|-|-|-|\n|**Full UniGRPO (Default)**|✓|Uniform-ratio|1:1:1|66.5|19.5|0.95|\n|w/o KL Constraint|✗|Uniform-ratio|1:1:1|66.1|19.8|0.95|\n|Random Masking|✓|Random|1:1:1|63.4|17.9|0.87|\n|Text Rewards↑|✓|Uniform-ratio|3:1:1|67.2|17.4|0.86|\n|MMU Rewards↑|✓|Uniform-ratio|1:3:1|64.5|20.5|0.91|\n|T2I Rewards↑|✓|Uniform-ratio|1:1:3|63.8|16.8|0.96|\n\nFrom the results above, we observe the following key findings: (1) The KL constraint has **minimal impact on overall performance**. It brings negligible improvement in text generation and does not significantly affect the multimodal components, aligning with recent GRPO-based works such as DanceGRPO [1] (2) Using a uniform-ratio mask scheduler **consistently improves model performance,** by better simulating the progressive noise levels in diffusion training. Please kindly refer to our **Appendix E.2**, where we reported our training curves using a uniform masking scheduler, further validating these quantitative results. (3) Varying reward weights across modalities leads to task-specific performance trade-offs. We use 1:1:1 for balance and simplicity, deferring optimization to future work. We will incorporate these findings in the final version to provide a more comprehensive analysis of UniGRPO and its effectiveness in training MMaDA. Thank you again for your valuable feedback.\n\n**Q2:How sensitive is performance to the relative weights of correctness, CLIP, and ImageReward signals?**\n\nA2: We thank the reviewer for raising this important point. We further analyze modality-specific reward weights.\n1. Correctness vs. Format Reward\nWe first examine how the balance between correctness reward and format reward affects performance in text reasoning and multimodal reasoning tasks. We conduct UniGRPO training for 4,000 steps from the same stage-2 checkpoint, varying the ratio between correctness reward and format reward across **1:1, 4:1, and 10:1** settings over **3 random seeds**. As shown in the table below, the 4:1 setting offers the best trade-off, preserving both performance and output consistency, and best stability across seeds with reduced variance.   \n**Table b: Ablations on Correctness vs Format Ratio of UniGRPO.**\n| Correctness:Format Ratio | GSM8K | MATH500 | GeoQA | Formatting Acc. |Std. |\n|------|-------|-----|--------|----------|----|\n| 1 : 1 | 65.1  | 26.1    | 14.0| **84.1%** |0.71%|\n| 4 : 1 (default) | **66.5** | **28.5** | **19.5**|83.6%  | **0.35%**|\n| 10 : 1 | 66.3  | **28.5**    |19.2| 73.9%    |0.64%|\n\n2. For image generation, we varied the ratio between CLIP Score and ImageReward across 1:3, 1:1, and 3:1, also over **3 random seeds**, and reported our ablation results as below:  \n**Table c: Ablations on CLIP vs ImageReward Ratio of UniGRPO.**\n|CLIP : ImageReward Ratio|CLIPScore(×0.1)|ImageReward|Avg.|Std.|\n|-|-|-|-|-|\n|1:3|3.01|**0.97**|1.99|0.62%|\n|1:1(default)|3.04|0.95|**2.00**|**0.58%**|\n|3:1|**3.05**|0.91|1.98|0.63%| \n\n   All three reward ratios consistently lead to performance improvements, indicating that the image generation branch is relatively robust to moderate changes in reward composition. Among them, the 1:1 setting achieves the most balanced results and exhibits the lowest variance across training seeds.\n\n**Q3: Adaptation for weaker LLM/VLM**\n\nA3: Thank you for this insightful question. While we use a strong LLM  to ensure high-quality reasoning, we would like to clarify that the **Mixed Long-CoT stage is model-agnostic**, and our framework is designed to be compatible with **any LLM/VLM capable of producing CoT traces**.\nTo evaluate its scalability and robustness in lower-resource or domain-specific scenarios, we conducted additional experiments using two alternative curation models:\n(1) **LLaMA3-8B**, representing a relatively weaker and smaller model compared to GPT4.1 in low-resource settings. (2) **Qwen2.5-Coder-7B**, a domain-specialized model for code generation.\nWe used these models to generate CoT traces for corresponding tasks (general reasoning or coding), and performed Mixed Long-CoT fine-tuning for 80,000 steps starting from our stage-2 checkpoint. Results are shown below:  \n\n**Table d: Ablations on Different LLMs for CoT Traces Curation.**\n| CoT Traces | GSM8K (Math) | MATH500 (Math) | HumanEval (Code) | MMLU (General) |\n| ------ | ----- | ----- | ----- | ------- |\n| MMaDA-stage 1 | 17.4     | 4.2     | 15.9           | 42.1      |\n| Generated by GPT-4.1  | **58.5**     | **20.5**      | 25.4             | **46.7**      |\n| Generated by LLaMA3-8B         | 49.3        | 18.2           | 21.8             | 43.7           |\n| Generated by Qwen2.5-Coder-7B  | -            | -              | **33.5**         | -              |\n\nAs seen, while the weaker model (LLaMA3-8B) leads to a moderate drop in absolute performance, it still enables **improvement of alignment over the baseline.** Meanwhile, the domain-specific model Qwen2.5-Coder-7B yields **stronger alignment in its respective coding domain**, highlighting the adaptability of our framework to different model sources and domains. We believe these results demonstrate that our training mechanism of MMaDA remains effective and extensible even in scenarios where state-of-the-art curation models are unavailable.\n\n**Q4: Any plans for model-generated self-CoT data?**.   \nA4: Thank you for the insightful suggestion. Given MMaDA’s unified understanding and generation capabilities, it naturally supports self-generated CoT data and **self-evaluation.**. \nAfter training, the model can generate its own reasoning traces, which can be filtered via (1) external validation (e.g., GPT-4.1), or (2) internal consistency checks—for instance, generating a caption from an image and then regenerating the image from that caption, with CLIP similarity as a self-evaluation signal.  \n\nThis self-bootstrapping approach is inspired by HermesFlow [2], showing that unified models can iteratively improve via reinforcement and self-generated data. We view this as a promising future direction and plan to explore it in subsequent work.\n\n**Q5: trade-off of retaining the MAGVIT-v2 VQ tokenizer versus adopting higher-resolution or learned continuous encoders**. \n\nA5: Thank you for pointing out this important consideration. Indeed, the VQ tokenizer presents limitations, and we elaborate on the trade-offs below:\n1. First, our primary goal is to establish a **unified diffusion backbone** as a proof-of-concept for future multimodal models. Using a fixed-resolution VQ tokenizer (f = 16) enables **faster convergence and more efficient training**, making it ideal for validating our framework and its effectiveness across modalities.\n2. For compression f=16, it's actually very common in image generation, for example,  SANA[3] compresses by 32X, but is still able to generate high-quality 4K images, and not a limiting factor for visual quality in generative tasks.\n3. For image understanding, we agree that continuous encoders may preserve finer visual details for understanding. Prior work, such as Show-o [4] demonstrates this advantage. However, we note two key challenges: (i) The performance gap is **partly due to data scale and training paradigm, not just architecture**—SigLIP benefits from massive image-text pretraining, while current VQ tokenizers are trained solely on reconstruction. (ii) More importantly, using continuous encoders for understanding but discrete encoders for generation introduces a **modality gap in visual representations**, potentially degrading consistency in tasks like **multi-turn dialogue.**\n\n    To address this, we plan to explore unified discrete tokenizers such as UniTok [5], which integrate alignment objectives during tokenizer training. This direction offers a promising path toward consistent, scalable multimodal representation learning beyond MAGVIT-v2.\n\n[1] DanceGRPO: Unleashing GRPO on Visual Generation.   \n[2] Hermesflow: Seamlessly closing the gap in multimodal understanding and generation.   \n[3] Sana: Efficient high-resolution image synthesis with linear diffusion transformers.  \n[4] Show-o: One single transformer to unify multimodal understanding and generation.  \n[5] Unitok: A unified tokenizer for visual generation and understanding."}, {"review_id": "TmibXxmV6L", "reviewer": "Reviewer_7Uo4", "summary": "MMaDA 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.", "questions": "- 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.", "limitations": "No. 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.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "I am raising my score from reject to borderline reject, contingent on the authors’ revised plan addressing my concerns. Nevertheless, the required revisions remain substantial, and my overall stance remains negative given the extensive modifications demanded by the initial submission. Overall, I believe the initial submission requires substantial revisions to address these misleading presentations and further improve the performance.", "rebuttal": "We sincerely thank the reviewer for your valuable feedback, and we address your concerns as follows:  \n\n**Q1: Overstated claims of MMaDA's performance.**\n\nA1:Thank you for the constructive feedback. We provide the following clarifications and revisions:\n1. Our core contribution lies not in surpassing all representative models, but in proposing **a unified diffusion-based backbone that supports three mainstream tasks** including text reasoning, multimodal understanding and image generation, along with **a fully open-sourced training and data curation pipeline**. This design is intended to promote reproducibility and further progress in unified multimodal modeling. To better reflect this intention, we will revise our claim to clarify that \"our model achieves **highly competitive performance, and state-of-the-art on some benchmarks, particularly when compared against models trained on publicly available data.**\" We deeply regret any unintentional misinterpretation our initial wording may have caused. \n2. We will also include missing models such as **Dream-7B (for only text generation) and Janus-Pro-7B (for multimodal understanding and generation)** in our final version, and **report their parameter counts**. Regarding these comparisons, we want to respectfully highlight some crucial factors. Many leading models, including Janus-Pro, are trained on **private and internally-curated datasets** that are not available to the public. Similarly, Dream-7B is **initialized from Qwen2.5-7B**, which is substantially stronger than our backbone LLaDA-8B. The pretraining data and process of Qwen2.5 are also undisclosed and are widely believed to be of significantly higher quality. The performance gap resulting from the quality difference between private and publicly available datasets can be considerable.\nWe primarily aim to provide a transparent, fully open-source alternative for a new line of backbone for the community.\n\n**Q2: Inconsistent benchmarking results of LLM**. \n\nA2: Thank you for your detailed observation and for pointing this out. In Tab.4 of our manuscript, we directly cited the numbers from **LLaDA's Table 1**, which reflect the base pretraining versions of the models, including Qwen2-7B. This is also consistent with our reported results for LLaDA-8B-base and our own MMaDA-8B. As you rightly mentioned in Weakness 4, our comparisons are made against **pretrained LLMs**, not instruction-tuned ones. The GSM8K score of 80.2 for Qwen2-7B-base is correct and consistent with other literature; it corresponds to the pretrained base version, not the instruction-tuned variant (which reaches 85.7). To clarify this and provide a more comprehensive view, we have now prepared a more detailed comparison that distinguishes between **base models** and **instruction-tuned models**, as shown below:  \n**Table a: Revised LLM Comparison**\n|Model|Arch|MMLU|ARC-C|TruthfulQA|GSM8K|MATH|GPQA|\n|-|-|-|-|-|-|-|-|\n|LLaMA2-7B-Base|AR|45.9|46.3|39.0|14.3|3.2|25.7|\n|LLaMA2-7B-Instruct|AR|44.1|57.3|-|29.0|3.8|28.4|\n|LLaMA3-8B-Base|AR|64.5|53.1|44.0|53.1|15.1|25.9|\n|LLaMA3-8B-Instruct|AR|68.4|82.4|78.3|29.6|31.9|-|\n|Qwen2-7B-Base|AR|70.3|60.6|54.2|80.2|43.5|30.8|\n|Qwen2-7B-Instruct|AR|-|-|-|85.7|52.9|34.3|\n|LLaDA-8B-Base|Diffusion|65.9|47.9|46.4|70.7|27.3|26.1|\n|LLaDA-8B-Instruct|Diffusion|65.5|88.5|-|78.6|26.6|31.8|\n|**MMaDA-8B(Ours)**|Diffusion|68.4|57.4|43.1|73.4|36.0|28.4|\n\nWe will revise the manuscript to clearly separate **base** and **instruction-tuned** model results to avoid further misunderstanding and ensure fair comparison across all baselines. \n\n**Q3: Comparison of Fairness of Table 4's LLM benchmarking.**\n\nA3: Following Q2 and A2, we would like to further clarify the **fairness** concern raised in Table 4's LLM comparison:\n1. First, our core contribution lies in the exploration of a unified diffusion-based training paradigm that supports **three mainstream tasks**, including text reasoning, multimodal understanding, and image generation, using only **open-sourced data and limited computational resources**. The goal of our work is not to claim absolute superiority over all existing models, but to demonstrate that it is possible to build a competitive unified model under constrained conditions, and to open-source the entire training pipeline to benefit the community. \n\n2. Regarding fairness, we respectfully argue that while our model includes multi-stage training such as mixed long-CoT and UniGRPO, **the comparison is actually unfair to MMaDA**. We humbly argue that to focus solely on our post-training stages overlooks the most critical factor in a large model's capabilities: **the pre-training phase**. As evidenced by previous works [1][2], the large volume and high quality of pre-training data determine a model's foundational intelligence. In this regard, there is a staggering disparity: Qwen2-7B was pretrained on **7 T tokens**, LLaMA 3 was pretrained on **15 T tokens**. In contrast, the first pre-training stage of our MMaDA used **less than 0.1 T tokens** from RefinedWeb.\nAlthough our model was initialized from LLaDA, integrating a new modality (vision) is not a trivial step. This process fundamentally perturbs the model's existing linguistic knowledge base, necessitating a significant relearning phase. This is quantitatively demonstrated in our ablation studies (Table 6), where our model's GSM8k score **drops from LLaDA's 70+ to just 17.4** after our initial multimodal pre-training. Furthermore, since we do not have access to LLaDA's proprietary training data, our newly introduced open-source datasets cannot seamlessly leverage the model's original knowledge.\n\n3. Regarding **post-training**, our instruction tuning relies entirely on open-source data, totaling fewer than 200K pairs. This stands in stark contrast to LLaDA’s 4.5M **meticulously curated, internal data pairs**. The difference in both quantity and, crucially, quality is immense.  Moreover, models like Qwen2-7B incorporate RL into their training but do not release the specifics or volume of their preference data, making a truly fair comparison with their internal alignment process impossible.\n\nTherefore, we respectfully suggest that the **real unfairness** lies not in our post-training, but in the **resource and data gap** between MMaDA and other stronger baselines. Our training pipeline is designed not to gain an edge, but to **bridge this gap** through carefully staged training. We hope the community sees this as an effort to make unified modeling more accessible and reproducible, despite the inherent challenges.\n\n**Q4: Refining related work.**\n\nA4: Thanks for your constructive suggestion. We plan to expand our Related Work section in main text as follows (**abbreviated version due to rebuttal limits**):\n\n**Multimodal Understanding**: ....... Early MLLMs such as LLaVA, MiniGPT-4 ...... primarily focused on understanding tasks by projecting features from pre-trained modality-specific encoders (e.g., CLIP) into LLM input spaces. These models demonstrated impressive capabilities in multimodal reasoning but were limited in their generation abilities.\n\n**Visual Generation**: Diffusion models have established themselves as the dominant paradigm........ These approaches excel at high-quality visual synthesis. Meanwhile, purely autoregressive methods, such as LlamaGen and VAR, have attempted to model visual content through sequential prediction but generally achieve lower generation quality than diffusion models......\n\n**Unified Vision-Language Foundation Models**: Recent unified models like SEED-X, DreamLLM, and Emu3 have demonstrated varying degrees of success....... However, a critical gap remains in the post-training optimization of these unified models.......This work addresses this limitation by developing novel post-training techniques that optimize unified multimodal foundation models for three generative tasks within a single, efficient framework.\n\n**Q5: In Table 5, the metrics for each task are limited to only one.**\n\nA5: Due to layout and space constraints, we only reported a single representative metric per task in Table 5. In the revised manuscript, we will include a more comprehensive set of metrics and expand the corresponding discussion accordingly.\nTo briefly illustrate, we present the full set of text task results below, with different denoising steps:\n|Denoising Steps|MMLU|ARC-C|TruthfulQA|GSM8K|MATH|GPQA|\n|-|-|-|-|-|-|-|\n|1024|66.9|57.4|43.1|73.4|36.0|28.4|\n|512|66.3|53.5|42.3|64.2|31.5|24.7|\n|256|65.7|50.2|39.6|42.8|23.8|21.3|\n\nWe observe that performance degrades across all tasks as denoising steps decrease, with **math-related tasks exhibiting the most pronounced drop**. This suggests that mathematical reasoning is particularly sensitive to step-wise denoising quality—errors introduced early in parallel decoding are more likely to compound. We plan to investigate **more stable denoising strategies** in future work to improve robustness on such tasks. And we will revise our manuscript to **include a more thorough discussion on sampling efficiency and openly acknowledge the current limitations.** We sincerely appreciate your thoughtful feedback and for bringing this important point to our attention.\n\n**Q6: Typos in the text** \n\nA6: Thank you for your careful observation. We will correct these typos accordingly.\n\n**Q7: What do \"higher-order solvers\" refer to in line 294?**\n\nA7: This refers to higher-order denoisers, such as **DPM-Solver[3]**, which leverages high-order numerical integration for efficient sampling. It prioritizes sampling on uncertain tokens with greater precision, potentially enabling faster decoding.\n\n**Q8: Sampling algorithm for MMaDA performance evaluation?**\n\nA8: We use semi-AR for sampling. \n\n[1] Scaling laws for neural language models.  \n[2] LLM post-training: A deep dive into reasoning large language models.  \n[3] Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Neurips 2022."}, {"review_id": "YXE8fHqldY", "reviewer": "Reviewer_fZ2o", "summary": "This 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.", "questions": "To 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.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**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", "was_revised": true, "final_justification": "The authors’ rebuttal has clarified the unique advantages of the MMaDA architecture compared to purely AR-based models. As a result, I am raising my rating to borderline accept. However, I agree with Reviewer 7Uo4 that the performance of MMaDA appears somewhat overstated; it does not demonstrate a clear advantage over state-of-the-art methods, even with an additional RL training stage. Additionally, I strongly encourage the authors to thoroughly revise the manuscript and convert the bullet-pointed content into well-structured paragraphs.", "rebuttal": "Thank you for your thoughtful review and for highlighting the strengths of our work, including the unified architecture and the proposed UniGRPO algorithm. We appreciate your recognition, and below we address your concerns in detail.\n\n**Q1: The paper lacks an in-depth discussion of the unique advantages offered by the proposed diffusion-based architecture compared to AR-based architectures**\n\nA1: We appreciate your insightful suggestion. In the revised manuscript, we will include an expanded discussion on the distinct advantages of our diffusion-based architecture over autoregressive (AR) models.  While both unified AR and diffusion models share a probabilistic generative formulation, unified diffusion models offer several unique advantages:\n1. **Substantially Imporved Sampling Efficiency**: \nA core limitation of unified AR-based models lies in the **inference latency for image generation.**  When designing a unified architecture, the goal is to approach the quality, fidelity, and efficiency of modality-specific models. However, AR models decode sequences in a left-to-right (raster-scan) fashion, leading to an O(N) inference cost for sequence length *N*. This results in significant delays during image generation. In contrast, diffusion models involve O(T) iterative denoising steps, where *T ≪ N*, making them notably faster in generation. \nWe compare generation time on an A100 GPU across two state-of-the-art AR-based unified models, Emu3 [1] and Lumina-mGPT [2], against our diffusion-based MMaDA:  \n**Tab.A: Comparison of Image Generation Time Cost**\n | Model      | Generation Time  | Resolusion|\n   | ---------- | ---------------- | - | \n   | Emu3       | \\~600 seconds       | 720p|\n   | Lumina-mGPT | \\~78 seconds      |512p|\n   | **MMaDA**  | **\\~15 seconds** |512p|\n\n   This efficiency gap makes AR-based unified models less practical for real-world deployment, particularly in latency-sensitive applications.\n\n2. **Empirical Performance Advantages**:\nTo isolate the impact of our diffusion-based formulation, we conduct a direct comparison with an AR baseline using LLaMA-3 8B as the shared backbone. The only difference lies in the training objective (AR likelihood vs. diffusion objective) and inference method (greedy sampling vs. diffusion decoding). Both models are trained on the same dataset with identical compute (64×A100 GPUs for 100K steps). We evaluate them on key generative benchmarks and also report image generation latency:  \n**Tab.B: Quantitative Comparison of Unified AR and Diffusion Architecture.**\n| Model                     | PPL ↓ | Image-Caption CLIP Score ↑ | FID ↓   | Image Generation Latency↓ |\n| ------------ | ---- | -------------------------- | ------- |-|\n| AR (LLaMA-3 8B, AR loss)  | **23.1** | 14.5                       | 16.2    |~120s|\n| MMaDA (Unified Diffusion) | 25.3 | **16.9**                   | **12.4** | **~7s**|\n      \n    These results demonstrate that **MMaDA outperforms the AR baseline on image generation and multimodal alignment**, while maintaining **competitive language modeling performance.** Most notably, MMaDA achieves **17× speedup in image generation latency**, highlighting the efficiency advantage of diffusion-based decoding. These results demonstrate that the diffusion-based decoding avoids the raster-order biases and error accumulation inherent to AR models, especially in vision tasks, and **largely improved efficiency**. While the PPL is slightly higher, we attribute this to the relatively underexplored nature of diffusion decoding for text generation.\n\n   Notably, recent work such as **UniDisc [3]** also explores unified modeling using discrete diffusion and reports similar trends: **notable improvements in image generation** and **comparable results in text generation** compared to AR-based models. This convergence further supports our design choice and conclusions.\n\n3. **Extensions of Capabilities**:\nAs detailed in Task Extension part of our Section 5, diffusion models inherently support **inpainting, outpainting, and text/image editing** without retraining, due to their denoising-based iterative structure. In contrast, AR models typically lack such capabilities without additional architectural or training modifications, limiting their flexibility in multimodal scenarios.\n\nWe thank the reviewer again for prompting this deeper analysis. The above discussion and results will be added to the revised version to clarify the distinct advantages of our diffusion-based unified modeling approach.\n\n\n**Q2: The comparison with SOTA models is incomplete in Tables 2 and 3.**\n\nA2: Thank you for pointing this out. We acknowledge that due to the rapid progress in this area, some relevant baselines were inadvertently omitted from the original submission. We will include these models—VILA-U, SynerGen-VL, Liquid, and MetaMorph—in our revised tables and update the corresponding discussions to ensure a more comprehensive and fair comparison. Their metrics are presented as follows:  \n\n**Tab.C: Additional Multimodal Understanding and Image Generation Results**\n| Model | POPE↑| MME↑|  VQAv2(test)↑| GQA↑| MMMU ↑ | MMB ↑| SEED ↑| GenEval↑|  WISE(Cultural) ↑ |\n| - | - | - | - | - | - | - | - | - | - |\n| VILA-U | 85.8 | 1401.8  | 79.4 | 60.8 | - | - | 59.0 | 0.57 | - | \n| SynerGen-VL | 85.3 | 1837  | - | 59.7 | 34.2 | 53.7 | 62.0| 0.61| -| \n| Liquid | 83.2 | 1448.0 | 76.8 | 61.1 | - | -| -| -| 0.38|\n| MetaMorph | 85.7 | - |  - | - | 41.8 | - | 71.8 | - | 0.58| \n| MMaDA | 86.1 | 1410.7 |  76.7 | 61.3 | 30.2 | 68.5 | 64.2 | 0.63 | 0.67 | \n\n**Q3: 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\nA3: We appreciate your detailed observations about the use of bullet points and bolded phrases. We would like to clarify that our initial intention in adopting this structured format was to allow readers to more clearly and quickly identify and follow the key innovations and contributions of our work. \nHowever, we fully acknowledge your valid concern that this formatting approach may have compromised the narrative flow and overall coherence of the paper. \nIn response to your feedback, we will make a comprehensive revision in the final version of our manuscript to convert bullet-pointed content into well-structured paragraphs and reduce the use of bolded phrases while ensuring that the innovative aspects of our work are clearly presented within a more traditional academic writing style.\n\n**Q4: To what extent does UniGRPO improve the model’s understanding and generation performance? A comparison with diff-GRPO would also be helpful.**\n\nA4: Thank you for the thoughtful suggestion. We would like to clarify that our UniGRPO framework is designed to demonstrate the effectiveness of reinforcement learning tailored for **diffusion models**. As such, we focused our evaluation on several representative tasks—including math reasoning, geometry reasoning, and text-to-image generation—which are particularly sensitive to reward-driven optimization **in Table 6 of Appendix E.1**.\n\nFollowing your suggestion, we have now extended our ablation to include a broader set of metrics aligned with those in Tables 2 and 3 of manuscript. The ablation results are summarized below and will be included in full detail in the revised manuscript.\n\n**Tab.D: Additional Multimodal Understanding Results after UniGRPO**\n| Model | POPE↑| MME↑| Flickr30k↑| VQAv2(test)↑| GQA↑| MMMU ↑ | MMB ↑| SEED ↑| \n| - | - | - | - | - | - | - | - | - |\n| MMaDA Stage 2 | 82.4 | 1368.5 | 63.3 | 74.5 | 57.5 | 25.3| 64.6| 61.9|\n| MMaDA after UniGRPO | 86.1 | 1410.7 | 67.6 | 76.7 | 61.3 | 30.2 | 68.5 | 64.2 | \n\n**Tab.E: Additional Image Generation Results after UniGRPO**\n| Model | GenEval ↑| CLIP Score ↑| Image Reward ↑| \n| - | - |- | -|\n| MMaDA Stage 2 | 0.61|29.4| 0.84 | \n| MMaDA after UniGRPO |0.63 |32.5  | 1.15 | \n\nAdditionally, we provide a direct comparison with diff-GRPO under matched conditions. While Appendix E.1 originally included the training curve, we now also report metrics for math reasoning, geometry, and image generation after the same 4000 steps training on 64X A100 to more concretely demonstrate the advantages of UniGRPO.   \n\n**Tab.F: Additional Comparison with diff-GRPO**\n| Model | GSM8K ↑| MATH500 ↑| GeoQA↑| CLEVR↑| CLIP Score↑| ImageReward ↑ |\n| - | - | - | - | - | - | - |\n|MMaDA Stage 2 | 65.2 | 26.5 | 15.9 | 27.5 | 29.4 | 0.84 | \n| diff-GRPO| 65.6 | 26.9 | 27.1 | 17.5| 28.3 | 0.91 |\n| UniGRPO | 66.5 | 28.5 | 19.5 | 27.8 | 30.2 |0.95 | \n\nThese results show that UniGRPO offers more stable convergence, higher reward alignment, and improved generalization across modalities. \n\nWe appreciate the reviewer’s insightful suggestion and will incorporate these expanded analyses and results into the revised version to more clearly illustrate the advantages of UniGRPO.\n\n[1] Emu3: Next-token prediction is all you need  \n[2] Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining.   \n[3] Unified multimodal discrete diffusion"}]

Author rebuttal:

Meta-review: The paper received mixed ratings from the reviewers. Most of the reviewers are positive about the submission, in terms of the technical contribution and the experimental evaluation. One reviewer raised several questions regarding the presentation and experimental details. These issues have been well addressed in the rebuttal and have been acknowledged by the reviewer. As the overall tone from the reviewers is supportive, AC finally decided to recommend acceptance of the submission.
Decision: Accept (poster)