MMaDA-Parallel: Multimodal Large Diffusion Language Models for Thinking-Aware Editing and Generation
Authors: Ye Tian
Ling Yang
JiongFan Yang
Anran Wang
Yu Tian
Jiani zheng
Haochen Wang
Zhiyang Teng
Zhuochen Wang
Yinjie Wang
Yunhai Tong
Mengdi Wang
Xiangtai Li
Venue: ICLR 2026 Poster (2026)
Keywords: generation
multimodal diffusion language model
Primary area: foundation or frontier models, including LLMs

Abstract: While thinking-aware generation aims to improve performance on complex tasks, we identify a critical failure mode where existing sequential, autoregressive approaches can paradoxically degrade performance due to error propagation. 
To systematically analyze this issue, we propose ParaBench, a new benchmark designed to evaluate both text and image output modalities. Our analysis using ParaBench reveals that this performance degradation is strongly correlated with poor alignment between the generated reasoning and the final image.
To resolve this, we propose a parallel multimodal diffusion framework, MMaDA-Parallel, that enables continuous, bidirectional interaction between text and images throughout the entire denoising trajectory. MMaDA-Parallel is trained with supervised finetuning and then further optimized by Parallel Reinforcement Learning (ParaRL), a novel strategy that applies semantic rewards along the trajectory to enforce cross-modal consistency. Experiments validate that our model significantly improves cross-modal alignment and semantic consistency, achieving a 6.9\% improvement in Output Alignment on ParaBench compared to the state-of-the-art model, Bagel, establishing a more robust paradigm for thinking-aware image synthesis.

Published as a conference paper at ICLR 2026
MMADA-PARALLER MULTIMODAL LARGE DIFFU- SION LANGUAGE MODELS FOR THINKING-AWARE EDITING AND GENERATION
Ye Tian
Ling Yang Jiongfan Yang'
Anran Wang"
Yu Tian
Jiani Zheng
Haochen Wang 2.4 Zhiyang Teng"
Zhuochen Wang²
Yinjie Wang
Yunhai Tong' Mengdi Wang
Xiangtai Li
'Peking University Bayce Princeton University 4CASIA The University of Chicago
Huggingface: MaAiD---rrlll--MMe Code: MMaDA-Parallll-Code
ABSTRACT
While thinking- awwre generation aims to improve performance complex tasks, we identify a critical failure mode where existing sequential, autoregressive ap- proaches can paradoxically degrade performance due to error propagation. To sys- tematically analyze this issue, we propose ParaBench, a new benchmark designed evaluate both text and image output modalities. Our analysis using ParaBench reveals that this performance degradation is strongly correlated with poor align- ment between the generated reasoning and the final image. To resolve this, we propose a parallel multimodal diffusion framework, MMaDA-Parallel, that en- ables continuous, bidirectional interaction between text and images throughout the entire denoising trajectory. MMaDA-Paralle is trained with supervised fine- tuning and then further optimized by Parallel Reinforcement Learning (ParaRL), a novel strategy that applies semantic rewards along the trajectory to enforce cross- modal consistency. Experiments validate that our model significantly improves cross-modal alignment and semantic consistency, achieving 6.9% improvement in Output Alignment on ParaBench compared the tfae-of-the-ard model, Bagel, establishing a more robust paradigm for thinking- aware image synthesis.
INTRODUCTION
Recent advances in multimodal generative models have achieved remarkable progress instruction- based image generation and editing (Esser et al., 2024a; Labs, 2024; Wei et al., 2024; Liu et al., 2025b). Given diverse textual prompis, these models can produce visually coherent and semantically aligned results across wide range of tasks. However, these models often struggle with complex instructions that require reasoning over world knowledge, frequently leading to incorrect editing and generation (Wu et al., 2025c; Niu et al., 2025; Zhao et al., 2025). To mitigate this gap, recent studies have introduced intermediate reasoning steps before visual generation (Fang et al., 2025; Jiang al., 2025a; Deng et al., 2025a). In these approaches, textual reasoning is first performed to guide subsequent image editing and generation. Such explicit reasoning has proven effective in improving the quality and consistency of image editing and generation (Deng et al., 2025a).
Despite the general effectiveness of incorporating a reasoning process prior to image synthesis, we observe counterintuitive and critical phenomenon. On certain benchmarks (Wu et al., 2025c), the inclusion of reasoning can in fact reduce the semantic fidelity of the generated images. For model starts with correct reasoning but then shifts to
example, in Figure (aa), a "thinking-awar refining minor details like background textures. This reduces attention on the primary subject and causes the final edit to misidentify it completely. The resulting image thus deviates from the user's core instruction and even contradicts its own thinking prompt, leading to a clear performance drop. This raises a crucial question: What underlies this performance degradation?
"Equal Contribution. "Coreponding Authors

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Published as a conference paper at ICLR 2026
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suntste, Mhser 6-15- stmoture the image shouls pumain the sume, foregiuas, and builoings the wwitain's shope atca-casped repucted to matoh Roont tijk.
Editing Proupt: Natija to fonss casi-capued
Bicelbe
Japan's
Sequental Thinking-wware Synthesis
Ressoning Gains Calegoriee
202
ALS
Perollel
Denates
(b) Parallel Thinking-Aware Synthesis
Output Aligmmant Roasoning and Acrae Categorics
Figure I: Sequential vs. parallel thinking-aware image synthesis. (a) Sequential generation (Bagel, GPT40) may suffer from vague incorrect reasoning. (b) Parallel generation aligns text and im- age at each denoising step, reducing hallucination and errors. (c) Quantitative comparison shows reasoning can degrade performance certain categories. (d) Poorer categories also exhibit weaker reasoning-imal alignment, highlighting the need for stronger cross- cross-modal alignment.
Based on these failure cases, we hypothesize that the degradation stems from the reasoning text itself. However, this hypothesis difficult to verify with existing benchmarks (Wu al., 2025c; Zhao et al., 2025). These benchmarks only evaluate the final image against the initial prompt, but cannot evaluate the intermediate reasoning step or its alignment with the final output.
Therefore, we introduce ParaBench, our new benchmark designed to explicitly evaluate this output alignment between a model's generated reasoning and its final image. Using ParaBench to evalu- ate the state-of-ther tfrtth--rtt model Bagel (Deng et al., 2025a), find strong correlation: performance degradation occurs precisely in categories where output aligmment weakest (Figure i(d). We attribute this to the compounding errors inherent in sequential autoregressive models, where am- biguous or incomplete reasoning provides unreliable guidance for the subsequent image generation, ultimately degrading the final output.
Thus, while pre-reasoning can in principle enhance multimodal generation, its reliance on an autore- gressive pipeline makes the process vulnerable another line of work has explored discrete diffusion models for text or image generation (Nie et al., 2025: Yang al., 2025a; Ye al., 2025a), which remove the token-by-toker constraint of autore- gression and instead employ confidence-basse spired by these advances, we ask: What if multimodal models could generate text and images in parallel? Such paradigm directly addresses the limitations of AR reasoning: text and images can attend to each other at every denoising step, avoiding the propagation of hallucinations and vague priors while grounding textual descriptions in visual evidence.
accumulation and semantic drift. Recently,
sampling to achieve greater global consistency. In-
Building on this insight, we propose a purely diffusion-based framework for parallel text-image generation, where cross-modal interaction is maintained throughout the trajectory to ensure robust and semantically faithful multimodal editing and generation, as shown in Figure 1(b)..
To train this framework, we first establish a thinking-aware data curation pipeline. We prompt a powerful VLM with data triplets ((iputt image, edit instruction, output image)) sourced from sidely-adopted image editing and generation datasets. The VLM is tasked to generate reason- ing trace that explains the edit process. This pipeline yields a training dataset of quadruplets: (input image, instruction, reasoning trace, output image), designed to elicit the models' reasoning
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and generation capabilities. We use this dataset to perform supervised fine-tuning on MMaDA (Yang et al., 2025a). This parallel version, MMaaA-Parallel, demonstrates higher output consistency compared to sequential basclines.
Notably, such consistency is observed not only in the final outputs but also throughout the gen- eration trajectory. We observe that during the parallel denoising process, the image region corre- sponding to a specific semantic concept is often refined simultaneously with its textual counterpart. However, standard SFT and conventional reinforcement learning algorithms optimize for the final outcome only. This output-level supervision is too coarse to enforce the fine-grained, stepwise alignment we observe and cannot guarantee consistency at intermediate steps. To fully leverage this trajectory-level consistency, we draw inspiration from process-level and trajectory-level optimiza- tion methods (Li & Li, 2024; Wang et al., 2025) and introduce Parallel Reinforcement Learning (ParaRI.). Instead of focusing solely on the final outcome, ParaRI. incorporates stepwise semantic supervision to refine alignment along the denoising trajectory. Our experiments demonstrate that this trajectory-level optimization provides a more granular and effective signal for diffusion models compared to traditional output-level supervision.
Extensive quantitative and qualitative results validate the effectiveness of MMaDA-Paralle, for thinking-wward image editing and generation, and further highlight the additional gains achieved through ParaRL. Our contributions be summarized as follows:
1. In-depth Benchmarking and Analysis of Thinking-aware Image Synthesis. We pro- pose ParaBench, which systematically evaluates thinking-awars aware image generation and edit- ing, focusing on text and image quality and their alignment.
2. Parallel Multimodal Diffusion Framework. We propose a purely discrete diffusion- based approach for parallel thinking-awaan image editing and generation, which enables bidirectional attention between modalities at every denoising step and effectively alleviates the error accumulation of autoregressive pipelines.
3. Parallel Reinforcement Learning. We introduce a parallel reinforcement learning strat- egy, ParaRL, which assigns semantic rewards along the denoising trajectory, further en- hancing alignment between the output modalities and the overall performance.
4. Extensive Evaluation and State-of-the-Arr Aligument. Our comprehensive experiments validate the framework, establishing ofte-of-thh-art performance among open-souree mod- els with 6.9% gain in Output Alignment over Bagel on our ParaBench benchmark, while maintaining comparable performance on single-modality metrics.
2 RELATED WORK
Recent progress in multimodal models for image understanding, generation, and editing has been rapid, yet most approaches remain constrained to single-modal generation conditioned on multiple modalities (Esser et al., 2024b; Wu et al., 2025a; Labs et al., 2025; Bai et al., 2025). To improve the accuracy and fidelity of multimodal generation, a growing line of work has explored introduc- ing textual Chain-of-Thie Thoughh reasoning process before image generation to this paradigm as thinking-awar aware image generation and editing. For instance, early efforts such as Chameleon (Team, 2024) and Mogao (Liao et al., 2025) investigated interleaved genera- tion, enabling interleaving sequences of text and image tokens. Image-CoT (Guo et al., 2025b) and GoT (Fang et al., 2025) incorporated CoT reasoning before image synthesis, demonstrating that reasoning traces can enhance generation quality. Bagel (Deng et al., 2025a) further extended this idea by integrating chain-of-thought reasoning into both image generation and editing, enabling more flexible and semantically aligned outputs. Building on this direction, follow-up works such as OmniGen2 (Wu et al., 2025b) and IRG (Huang et al., 2025a) introduced reflective reasoning after image generation, using multi-turn textual feedback to refine visual outputs iteratively. Most existing methods, however, rely on a sequential autoregressive interleaved pipeline, which could limit direct cross-moda interaction and make the model prone to eπror accumulation from imperfect reasoning traces. Exploring a parallel generation framework that enables more interaction within output modalities is still lacking in this scenario. (More related work can be found in Appendix C).
editing. We refer
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Table 1: Thinking may degrade the performace of visual synthesis. Bagels' performance com-
parison on ParaBench editing tasks with and without thinking. We also report the reasoning quality (Text Qual.) and cross-modal alignment (Output Align.).
Editing Category
w/o Thinking
w/ Thinking
A (w/
w/o)
Text Qual.
Ouiput Align.t
Temporal
72.3
75.6
+3.3
92.6
57.3
General
68.9
71.4
+2.5
86.2
58.1
Causal
70.1
67.2
22.9
75.3
46.2
Knowledge
74.5
76.8
+2.3
87.8
55.5
Spatial
69.8
65.0
-4.8
73.2
45.2
MMAAD-PARALLEL
3.1 FINDINGS AND BENCHMARKING ON THINKING-AWARE SYNTHESIS
To investigate whether pre-generation reasoning genuinely enhances performance, we conduct controlled study on image editing tasks, which provides clearer instruction evaluation than naive synthesis. We sample inputs from established benchmarks (Wu et al., 2025e; Zhao al., 2025) and generate paired outputs using Bagel (Deng et al., 2025a) an advanced, open-source, unified model supporting thinking-awand generation- wwtt and without thinking. We report the average editing evaluation metries in Kris-Bench (Wu et al., 2025c) in Figure I(c) and also Table 1.
Findings. While the reasoning step enhanced performance on most tasks, a notable countertrend emerged: performance declined in significant subset of cases, about 23%, particularly in complex compositional edits. closer analysis reveals that these failures often stemmed from low-quality vague reasoning text, which misguides the image generation process. This exposes critical gap in existing protocols: they evaluate the final image but ignore the quality of the intermediate reasoning- the other generated modality.
Benchmarking mixed modalities. This analysis reveals a fundamental limitation in current eval- uation paradigms: existing benchmarks (Wu et al., 2025c; Zhao al., 2025; Ghosh al., 2023) only evaluate images, ignoring the quality of the reasoming itself and its consistency with the im- age. To address this gap, we introduce ParaBench, a new benchmark specifically designed for the comprehensive evaluation of thinking-aware image synthesis. ParaBench comprises 300 challeng- ing prompts, split into 200 for editing and 100 for generation. The editing prompts are meticulously curated to test a wide spectrum of abilities, covering not only general operations (e.g., add, remove, replace) but also complex tasks requiring reasoning. The 100 generation prompts focus on open- ended creative synthesis of complex scenes, We evaluate models ParaBench using the GPT-4.1 across six fine-gre -eeegraiddd aspects: for the textual output, we assess Text Quality and Text Alignment; for the visual output, evaluate Image Quality, Image Alignment, and Image Consistency; and finally, the overall Output Alignment between them. More details are included in Appendix G.
To demonstrate ParaBench's diagnostic capabilities, apply it to representative baseline, Bagel. While full quantitative results are presented in Sec A, Table highlights a crucial finding by focusing on two key metries: Text Quality and Output Alignment. The results reveal clear correlation between the quality of the reasoning step and the final performance. Notably, the categories that exhibited performance degradation also suffered from significant drops in both reasoning quality and reasoning- aasoning-image synergy. This pattern strongly suggests that poor reasoning does not merely fail provide helpful guidance but actively misleads the generation process, validating the necessity of explicitly improving the synergy between text and image generation.
Motivations on parallel multimodal diffusion. Our benchmarking results reveal critical limi- tation in current thinking-aware generation: the sequential generation paradigm, where reasoning precedes image synthesis, creates a rigid dependency that can propagate errors and limit cross- cross-modal synergy. When reasoning quality degrades, directly undermines the subsequent image generation, as demonstrated by the correlated performance drops in spatial and temporal editing tasks. To ad- dress this fundamental issue, we propose parallel unified multimodal diffusion framework that enables simultancous generation of both reasoning text and images, fostering genuine multimodal collaboration while eliminating the error propagation inherent in sequential approaches.
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3.2 BASIC ALGORITHM AND ARCHITECTURE
(a) Training
Sampling
Image Response
Response
Jent
Paes
Parallel Decoding
Ieet
leol
lesti
Uniform Mask Predictor
Parallel Decoding
Jeail
Ionn
Inst
Imope Responde
Text Response
Paralle! Training with L El Exjes (x,1])
Parallel Sampling from pu(xa)ss)
Figure 2: Parallel Generation Architecture: During (a) training, image and text responses are masked and predicted in parallel with a uniform mask predictor, optimized by the masked token likelihood objective. During (b) sampling, the model performs parallel decoding to generate both image and text responses jointly, enabling efficient multimodal response generation.
Discrete diffusion models have demonstrated strong performance for both image and text genera- tion (Bai et al., 2024; Nie et al., 2025; Zhu al., 2025). Building the unified discrete-diffusion view, MMaDA (Yang et al., 2025a) demonstrates that a single diffusion framework can jointly model multiple modalities; however, its decoding remains sequential across modalities. To overcome this limitation, we propose a parallel multimodal diffusion framework that: (i) represents all modalities as discrete tokens, (ii) arranges them in interleaved sequence with bidirectional attention, and (iii) employs a single mask predictor shared across modalities, enabling synchronous denoising for both text and images. overview of this framework shown in Figure 2.
Interleaved discrete sequence layout. Following the MMaDA framework (Yang al., 2025a), we process both text and images within a unified discrete token space. Specifically, we tokenize text using the LLaDA tokenizer (Nie al., 2025) and encode images into grid of discrete visual tokens using pretrained MAGVIT-v2 (Yu et al., 2023) quantizer. These tokenized modalities are then serialized into single interleaved sequence, using explicit sentinels and task tags to enable full bidirectional cross-modal attention:
Input: < <|task/><|l Output: <|soi [output img] < eoi |>< bos [output text] loss
[img] < |><ii/>><bbb |> [text] ] |eos|>
During training, concatenate the input and output templates into single sequence, allowing the model to attend from outputs to inputs within unified context. The task token < ttak is instantiated differently depending the scenario, with < </thinkgen >> used for thinking-aware generation and < </thnkeditt >> used for thinking-aware editing. This single-sequence design eliminates the ordering asymmetry and exposure bias introduced by autoregressive cross-modal pipelines.
Training objective. Let To E {1,..., VJ denote the concatenated training sequence (input part followed by output part), where is the total number of tokens in the sequence. We keep the input part static and apply noise only to the output part. At a sampled timestep f E {l...., token in the output part we replace it with [MASK] with probability 3r and keep it unchanged with probability - :r; tokens in the input part are left unchanged:
11....,T,, T), for each
(8)
if i in input,
(
(
with prob. (1 3,), [MASK] with prob. 3r if í in output.
Equivalently, for positions in the output, the absorbing state marginal after t steps is q(z; x0) 0 (1 11-a;) a,) m where a, 11 - Bx), and m is the one-hot distribution of [MASK].
The parallel diffusion model po(: xx) is formulated as a unified masked-token predictor over the joint vocabulary of text and image tokens. Let i 1 1...., T, denote token positions in the
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Denoising from t:1 to t:0
Parallee Reinforcesent Learning
Projectory
Nit
Editing Prospt: Chonge tne cou into onicers
CUPGg lead) Elp
Ra
Rcz
Ree
Trajectory Average
86
R
Aut
mean m-mann (Aadal)
std
Policy Upgrading
Trajectory 2
((0
ΣΣ ng(o) (orkk)
Trajectory
-PKL(xplllonall
Figure 3: Overview of our proposed Parallel Reinforcement Learning (ParaRL). Rather than op- timization only to the final denoised outputs, ParaRL introduces reward signals along the entire denoising trajectory, reinforcing semantic alignment consistently throughout the generation process.
concatenated onput-output sequence. Since only the output segment noised during diffusion, the model predicts ground-truth tokens 2o at the currently masked positions within this segment. To better balance the training dynamics across modalities, we make the tmesstp-ddepnnder loss weight modality-speciee tokens in the output image segment and the output text segment are assigned separate weights, Wimg(!) (t) and Wh) For compactness, we write the objective using unified token-aware weight function w(l, i). We optimize timestep-reweiigheed cross-entropy:
purrallel (0) --E,
EE, mo,
Σ w(t, 1 [MASK]] log po(= (0) mi)
(2)
where [[:] is the indicator function and
Wing (t),
if lies in the output image segment,
w(t, i)
Whext (!),
if lies in the oulpul text segment.
weighting (tea(t) = 1/t for text tokens and a constant weighting Wime (t) = 1 for image tokens substantially stabilizes the training of image quality and output alignment. We illustrate this process in Figure 2(a) and include detailed additional preliminaries with ablations in Appendix D.
We empirically find that applying comestep-dependde
Parallel denoising with dual schedulers. Decoding proceeds along shared diffusion time axis ty - ... - to- as is shown in Figure 2(b). We define two modality-specine schedulers, Wing (l), Uheu(() (1) E (0, 1], which specify the target proportion of unmasked tokens at step At each re- verse step: (i) the model jointly predicts distributions for all currently masked positions; (ii) for each modality, a fraction tokens is sampled (e.g., s. via confidence-bur positions retained as [MASK] Because attention is bidirectional across the entire sequence, text and image can inform each other at every step of decoding. In our experiments, the text schedule implemented fully linear reveal schedule combined with semi-autoregressive confidence-bart based decoding Nie et al. (2025), while the image schedule follows a cosine reveal schedule with global confidence-based decoding. More details be found in Appendix E.
sampling). while the remaining
3.3 POST TRAINING WITH PARALLEL REINFORCEMENT LEARNING
Supervised Finetuning for Parallel Synthesis A key challenge in our approach is that existing generation and editing datasets lack the reasoning traces required for our parallel synthesis frame- work. To address this, construct a suitable training dataset by first aggregating samples from various sources. For each sample comprising an input image (for editing tasks), an instruction, and the final output image, employ multimodal LLM (Qwen-2.5-VL in implementation) to generate a corresponding reasoning trace. Further details on the dataset construction process, in- cluding the sources and categories, are provided in Appendix F. We then this dataset to perform

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supervised fine-tuning MMaDA (Yang et al., 2025a). This process adapts it into parallel variant capable of performing thinking-aweer aware synthesis, where reasoning and generation occur concurrently.
Synergy along the denoising trajectory. While analyzing generations from the finetuned model, we observe that certain semantic concepts emerge syn- chronously in text and image intermediate denois- ing steps. As illustrated in Figure 4, when tasked to change shirt to "vibrant rainbow color," the specific color words and their corresponding visual features appear at the same timestep. This observa- tion leads to a key insight: cross-modal alignment not an endpoint phenomenon but is progressively established throughout the generation trajectory. This implies that supervision applied to these inter- mediate steps, not just the final output, can further improve this alignment.
The atrrt ahould scripes in prisery colors (red, yelloo, green, blue. todigo,
ame hortoontnt
stripes in
colore
Denoise
Imoge Denoise
Denoising Step 4/11
Denoising Step 5/21
Figure 4: prompt: "'hange the blue shirt to vibrant rainbow color," the specific color decoding in text and image emerges the same step.
Synergy of sampling. Given the
Parallel reinforcement learning with trajectory optimization. Building on this insight, we fur- ther introduce Parallel Reinforcement Learning (ParaRL), novel training paradigm that directly leverages this intermediate cross-modal synergy. In- stead of rewarding only the final output, ParaRI. uses the alignment between text and image tokens at each denoising step as a dense reward signal.
Specifically. for a given query Q, the generated response a full trajectory Ti - (r.())...., To (Ix))), where |r|] is the total number of denoising steps and T(( is the set of tokens decoded step f. While this formulation provides a step-wise reward ri, for each intermediate response (t), optimizing over the entire dense trajectory is computationally prohibitive. To make training feasible, we adopt a sparse optimization strategy. During each online rollout, we pre-select and only compute
sampling steps and fix subset of step indices S C {l...., |.|S] wards ri, and their corresponding standardized advantages A,, for timesteps t E S. We adapt a diffusion GRPO objective (Gong et al., 2025) that accommodates token-level likelihood ratios with advantages calculated at these sampled steps:
G ΣΣ
To(o Q, (1 1)) -1)) Kola(o T1:t 1)) Ast)) )]
Jpolicy/ (0) = E
Σ C. oEr(()
(T) (-)(1112
Qu
1=1 LES
(T((t]
(3)
- KL IT Told]
where (r, 2 min( clip(r, 1 1 1 + e) A). In this objective, the summation is performed over the sparsely sampled steps f S. The term o ranges over all tokens within the state (t) at sampled step I, and Tal1: - 11) denotes the full history of tokens generated prior to step 4. Finally, Tald is the behavior policy for generating rollouts, and controls the KL penalty strength.
Trajectory reward design. In typical trajectory-level optimization frameworks, well-trained process reward model (PRM) (Li & Li, 2024) or value function Wang et al. (2025) is often required, since intermediate partial outputs usually lack sufficient semantic information for reliable evalua- tion. Surprisingly, in our parallel text-image generation setting, we find that intermediate fragments are already semantically meaningful. For instance, even partially decoded text tokens often reveal enough semantic cues to compute alignment with the simultancously generated image content, as illustrated in Figure 3. This observation allows us to bypass the need for a dedicated PRM: we directly employ semantic alignment between text and image as the reward signal.
Unlike tasks with binary rewards (e.g., mathematical reasoning), our cross-modal alignment ob- jective provides continuous reward signal. However, the naive CLIP score, which serves reward source, can exhibit high variance and an arbitrary scale, making it unstable for direct use in reinforcement learning. To ensure training stability, we therefore apply normalization scheme

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Table 2: Main results on ParaBench. Evaluation across editing and generation tasks. For non-
thinking image editing or generation models, text evaluation and output alignment are unavailable.
Model
Text Qual.
Text Align.
Image Cons.
Image Align.
Image Qual.
Oulput Align. Overall
Open-source models (Non-thinking)
D-ve
65.2
77.5
Owen-Image
84.2
Flux.I-Konee I-Kontext
77.9
65
84
Qwen- Image- Fdiit
78.2
73.5
84.1
Bagel (w/o think)
72.2
50.3
80.1
Closed-source models
GPT-4o
92.5
93.4
86.2
85.7
88.1
69.5
85.9
Gemini-2.5
94.1
95.2
88.5
90.2
Open-suucce models (Thinking-wwann)
Bagel think)
82
70.5
Show-o" (tuned)
75.2
20.7
69.1
57.5
78.5
48.9
66.6
SMaDA-Paralld w/o ParaRL.
76.5
70.4
70.5
80.5
51.5
67.9
MMaDA-Parallee w/ ParaRL.
80.4
71
73.4
63.2
81.2
59.8
71.5
inspired by prior work in RL with continuous rewards (Liu et al., 2025a). We begin by estimating the mean /CLU and standard deviation OCLP of CLIP scores across the training distribution, where we compute on a random 1 % subset of the data. Let C₁,t RCLIP (text( (t)), image(r; (t))) be the raw CLIP score for the content generated at step t. We first standardize this score to obtain ć using &,c Cue-Meeu This standardized score is then clipped to the range [-1, and linearly rescaled to yield the final reward Ri which is bounded within [0, 1]:
deLP
R4 2 (1 + clip(C 1))
(4)
The corresponding advantages Ar used in Eq. 3 are then obtained by standardization over the R2, maa--maa((( (12 12m)) std( (f11))5-11)
rollouts: Ai,
EXPERIMENTS
4.1
IMPLEMENTATION DETAILS
Training and datasets. Our final model, MMaDA-Parallel, is trained in two-stage process. We begin with supervised finetuning (SFT) on the MMaDA-MixCoT model, which integrates a LLaDA- 8B text backbone with MagVIT-v2 image tokenizer. For this stage, construct 150K thinking-and awaaee multiple existing benchmarks. In the second stage, we apply reinforcement learning with GRPO- based objective. To enhance training efficiency, this RI. stage focuses on the most challenging 10% of the SFT examples, optimizing the policy online improve cross-modal semantic alignment. More details of the dataset and training details can be found in Appendix F and H.
dataset of aware image editing and generation pairs, meticulously sourced and filtered from
Evaluation setup. We conduct our primary evaluation on the ParaBench benchmark, which was introduced in the Method section. We employ an LLL--as-a-judee framework (GPT-4.1) to assess performance across the six fine-grained metrics previously described, covering text quality, im- age fidelity, and cross-modal alignment. The prompts used for the LLM judge are detailed in the Appendix G. Our MMMaD--Paralle is compared against state-of-the-an thinking-awe including Bagel (Deng et al., 2025a), GPT-4o, and Gemini-2.5, well leading image-only gen- erators like Qwen-Image (Wu et al., 2025a), Qwen-Image-Eade (Wu et al., 2025a), Flux.1-des (Labs, 2024) and Flux. Konttex (Labs et al., 2025).
models,
4.2
MAIN RESULTS
Table 2 reports the overall performance our ParaBench benchmark. Our proposed method, MIMaDA-Paralles achieves the highest Output Aligument among all open-source models, confirm- ing the effectiveness of its parallel multimodal decoding and trajectory-leve optimization. In terms of general text and image quality, MMaDA-Parall performs on par with Bagel, despite Bagel be- ing trained dataset nearly three orders of magnitude larger. Compared leading closed-source
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Input
Bagel
(w/ think)
MMaDA-Parallel
Show the plant
silting tria lack
sime del uplaci are sate
lage sadd lncple virled vileeei
i ard flame
dints
nit de boge wileaeiiyy the Hige
Replace Hos
cop
nith umallaz ooclse miis botzang the Japios totart. The tayour
keyboar
and emmcheee the laage chould cofdee placed in the sura koybcard telor oweld be arjustedd gaay to match the usse
and
Generate pictuze stouup individuals
arr lounig
is in brightly ise park, thenis thzowing frishee.
Eaih bullived
diie ttat
shodd
oteer
sife
model sholld farn
Genexate a picture of a tall, gray stone clock tonez with teo clock
neceendii Ldban encoomene
sall,
faces rises above an industrial asea and distant undez
with
isees la aer and wt cuttt cotpicaae
lape (ottterreiaattt with siatte adects toar
doses
sky.
sigu
Figure 5: Qualitative results in comparison with Bagel.
models like GPT-4o and Gemini-2.5, MMADA- substantially narrows the gap alignment metrics while maintaining competitive text and image quality, demonstrating remarkable data effi ciency. Furthermore, the results indicate that our ParaRL stage consistently improves output text- image consistency, suggesting that trajectory-level optimization effectively strengthens cross-modal grounding throughout the generation process.
In addition, we provide qualitative comparison with open-source models in Figure 5, showcasing examples of both editing and generation. A key observation is that MMaDA-Parallel produces more precise and descriptive reasoning traces. This enhanced reasoning leads to superior visual fidelity in the final image. For instance, our model accurately renders complex instructions like a "melt- ing cake" and correctly applies causal reasoning to depict "withered grass." Moreover, MMaDA- Parallel demonstrates stronger compositional abilities, particularly in counting, correctly generating "three people" "two faces of a clock" where Bagel often fails. In contrast, Bagel's reasoning in these challenging cases tends to be vague or omits crucial details, leading to inaccurate image syn- thesis. These results further underscore Maralll 's capability for advanced thinking- aware editing and generation, driven by better-aligned semantic information.
producce
4.3 ANALYSIS OF KEY CONTRIBUTIONS
Table 3: Parallel vs sequential decoding.
Table 4: Output trajectory-Ievel RL.
Denoising
Text Align.
Image Align.
Output Align.
Model
Text Align.
Image Align.
Output Align.
Sequential
70.6
56.1
48.9
before RL
70.4
58.2
51.5
Parallel
70.4
58.2
51.5
w/ Output-level RI.
w/ ParaRL (Ours)
30.7
71
63,2
59.8
53.6

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Published as a conference paper at ICLR 2026
Table 5: Ablation on sampling steps ParaRL.
ParaRL
Text Qual.
Text Align.
Image Cons,
Image Align.
Image Qual.
Output Align.
Overall
Before RL
76.5
70.4
70.5
80.5
67.9
ParaRL.s= s=2
77.9
70.3
71.5
62.8
80.7
53.6
68.6
ParaRL (am3) (defauit)
73.4
63.2
ParaRL (s=4)
80.5
70.8
73.2
63.5
80.8
58.7
71.3
After presenting the overall results, we now return to two central research questions that moti- vated our work: RQ1: Does parallel denoising improve generation quality compared with sequential denoising? RQ2: Does trajectory-lewe finetuning improve over output-level finetuning?
The Benefit of Parallel Decoding (RQ1). We compare our model against a sequential baseline (MMaA MMMaA-Sequentiall that generates text before images. During training, noise was applied to only one modality at time to align with this sequential inference process. Table 3 shows our parallel framework substantially outperforms this baseline on key alignment metrics, with comparable text and image quality. This result validates our hypothesis: simultancous, interactive decoding is crucial for reducing error propagation and producing coherent multimodal outputs.
The Benefit of Trajectory-ILevel Optimization (RQ2). We compare two reinforcement learning strategies: (i) output-level RL, where rewards are computed on the final generated sample, and (ii) proposed ParaRL with trajectory-level finetuning, where rewards are aggregated across denois- ing steps. As shown Table 4, trajectory-lever optimization yields gains in text-image consistency and output alignment, and Figure 6 further shows that it enables more stable training dynamics.
Another key hyperparameter in this strategy is the number of sampled steps. s. We analyze its impact in Table 5 and report the training curve in Figure We find that using substantial improvements over = 2, as a denser reward signal provides more stable guidance. We adopt = 3 in the final configuration for the best balance between performance and efficiency.
4 yields
0.60
trajectory
5-3
0.58
0.58
5=2
0.56
0.54
Reward
0.54
Reward
0.52
0.52
0.50
0.48
0.48
0.46
1000
2000
3000
4000
5000
1000
2000
3000
4000
5000
steps
steps
Figure 6: ParaRL reward training curve between trajectory and output level optimization.
Figure 7: ParaRI. reward training curve across different sampling steps of the trajectory.
CONCLUSION
In this work, we investigated critical phenomenon where sequential thinking- models can paradoxically suffer from performance degradation on complex tasks. We conducted an in-depth analysis using our proposed ParaBench benchmark, which uniquely evaluates both output modali- ties, and found a strong correlation between this degradation and poor alignment between the gener- ated modalities. To resolve this, we propose parallel multimodal diffusion framework trained with supervised finetuning and further optimized by Parallel Reinforcement Learning (ParaRL) novel method of applying rewards along the entire denoising trajectory. Experiments validate that our approach significantly improves cross-modal alignment and semantic consistency, establishing more robust paradigm for thinking-award image synthesis.
10

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Published as a conference paper at ICLR 2026
Acknowledgment. This work is supported by the National Key Research and Development Program of China (No. 2023YFC3806600.
ETHICS STATEMENT
This work advances research in text and image generation. We acknowledge that such models may be misused to create deceptive or harmful content, such as falsified images or misleading infor- mation. Our study is conducted for scientific purposes, and encourage responsible use with appropriate safeguards to mitigate potential misuse.
REPRODUCIBILITY STATEMENT
We provide detailed training implementation details in Appendix H and main training code in the supplementary. All code and data are made public at
REFERENCES
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in neural information processing systems, 34:1 1e:1799-11999, 2021.
Jinbin Bai, Tian Ye, Wei Chow, Enxin Song, Xiangtai Li, Zhen Dong, Lei Zhu, and Shuicheng Yan. Meissonic: Revitalizing masked generative transformers for efficient high-resolution text- to-image synthesis. arXiv preprint arXiv:2410.0826/ 2024.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502. 13923, 2025.
Tim Brooks, Aleksander Holynski, and Alexei Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 18392-18402, 2023.
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image transformer. In CVPR, pp. 11315-1 11115-1132,,,1 2022.
Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang. Kevin Murphy, William T Freeman, Michael Rubinstein, et al. Muse: Text-to-image gen- eration via masked generative transformers. arXiv preprint arXiv:2301.08 000044 2023.
Junying Chen, Zhenyang Cai, Pengeheng Chen, Shunian Chen, Ke Ji, Xidong Wang, Yunjin Yang, and Benyou Wang. Sharegpt-4o-image: Aligning multimodal models with gpt-4o-level image generation. arXiv preprint arXiv:2506.11800 2025a.
Liang Chen, Lei Li. Haozhe Zhao, Yifan Song, and Vinci. RI-v: Reinforcing super generalization ability in vision-language models with less than $3. https: httggtttthhhcooo com com/ Seeep--Ae Deep-Agent/ R1-V, 2025b. Accessed: 2025-02-02.
Ting Chen, Ruixiang Zhang, and Geoffrey Hinton. Analog bits: Generating discrete data using diffusion models with self-conditioning. arXiv preprint arXiv:2208.0422 04202, 2022.
Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, et al. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505. 146688,,, 2025a.
Yihe Deng, Hritik Bansal, Fan Yin, Nanyun Peng, Wei Wang, and Kai- Wei Chang. Openvlthinker: An early exploration to complex languuee reasoning via iterative self colf-improveeent, 2025b. URL https: / //ar/ /arxiv oorgor//aaa..... 2503. 17352.

---

Published as a conference paper at ICLR 2026
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference machine learning, 2024a.
Patrick Esser, Sumith Kulal, Andreas Blatimann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference machine learning, 2024b.
Rongyao Fang, Chengqi Duan, Kun Wang. Linjiang Huang, Hao Li, Shilin Yan, Hao Tian, Xingyu Zeng, Rui Zhao, Jifeng Dai, al. Got: Unleashing reasoning capability of multimodal large language model for visual generation and editing, arXiv preprint arXiv:2503. 10639, 2025.
Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. Advances Neural Information Processing Systems, 36: 2023.
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong. Diffuseq: Sequence to sequence text generation with diffusion models. arXiv preprini urXiv:2210.089333 2022.
Shansan Gong, Shivam Agarwal, Yizhe Zhang, Jiacheng Ye, Lin Zheng, Mukai Li, Chenxin An, Peilin Zhao, Wei Bi, Jiawei Han, et al. Scaling diffusion language models via adaptation from autoregressive models. arXiv preprint arXiv:2410. 17891, 2024.
Shansan Gong, Ruixiang Zhang, Huangjie Zheng, Jiatao Gu, Navdeep Jaitly, Lingpeng Kong, and Yizhe Zhang. Diffucoder: Understanding and improving masked diffusion models for code gen- eration. arXiv preprint arXiv:2506. 20639, 2025.
Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo. Vector quantized diffusion model for text-to-image synthesis. In CVPR, pp. 10696- 10706, 2022.
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in Ilms via reinforcement learning. arXiv preprint arXiv:2501.1294 2025a.
Ziyu Guo, Renrui Zhang, Chengzhuo Tong, Zhizheng Zhao, Rui Huang. Haoquan Zhang, Manyuan Zhang, Jiaming Liu, Shanghang Zhang, Peng Gao, et al. Can we generate images with cot? let's verify and reinforce image generation step by step. arXiv preprint 25X1:22501.13926 2025b.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
Jixiang Hong, Yiran Zhang, Guanzhong Wang. Yi Liu, Ji- R-Rong Wen, and Rui Yan. Reinforcing mul- timodal understanding and generation with dual self-rewards. arXiv preprint arXiv:2506.0799 079663, 2025.
Wenxuan Huang, Shuang Chen, Zheyong Xie, Shaosheng Cao, Shixiang Tang, Yufan Shen, Qingyu Yin, Wenbo Hu, Xiaoman Wang, Yuntian Tang, et al. Interleaving reasoning for better text-to- image generation. arXiv preprint arXiv:2509.0665 06945,, 2025a.
Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Yao Hu, and Shaohui Lin. Vision-rl: Incentivizing reasoning capability in multimodal large language models. arXiv preprint (5l.:25003.07449 2025b.
Mude Hui, Siwei Yang, Bingchen Zhao, Yichun Shi, Heng Wang, Peng Wang, Yuyin Zhou, and Cihang Xie. Hq-edit: high-quality dataset for instruction-base image editing, arXiv preprint arXiv:2404.09999. 2024.
Dongzhi Jiang, Ziyu Guo, Renrui Zhang, Zhuofan Zong, Hao Li. Le Zhuo, Shilin Yan, Pheng-Ann Heng, and Hongsheng Li. T2i-r1: Reinforcing image generation with collaborative semantic-level and token-level cot. arXiv preprint 20X.022505.00703, 2025a.
12

---

Published as a conference paper at ICLR 2026
Jingjing Jiang, Chongjie Si, Jun Luo, Hanwang Zhang, and Chao Ma. Co-reinforcement learning for unified multimodal understanding and generation. arXiv preprint arXiv:2505.17534 2025b.
Black Forest Labs. Flux, 2024. URL https: /ttp::::////hh//tt//he http///ttth....mmmlaaa/aaaeaeeet
flux.
Black Forest Labs, Stephen Batifol, Andreas Blattmann, Frederic Boesel, Saksham Consul, Cyril Diagne, Tim Dockhorn, Jack English, Zion English, Patrick Esser, Sumith Kulal, Kyle Lacey, Yam Levi, Cheng Li, Dominik Lorenz, Jonas Müller, Dustin Podell, Robin Rombach, Harry Saini, Axel Sauer, and Luke Smith. Flux.1 kontext: Flow matching for in-context image generation and editing in latent space, 2025. URL //axxi. ozg/ abs /2506 15742.
Shufan Li, Konstantinos Kallidromitis, Hritik Bansal, Akash Gokul, Yusuke Kato, Kazuki Kozuka, Jason Kuen, Zhe Lin, Kai- Kai-Wei Chang, and Aditya Grover. Lavida: large diffusion language model for multimodal understanding. arXiv preprint arXiv:2505.16839 2025.
Wendi Li and Yixuan Li. 1.Xi::2410.11287, 2024.
Process reward model with q-value rankings. arXiv preprint
Chao Liao, Liyang Liu, Xun Wang, Zhengxiong Luo, Xinyu Zhang, Wenliang Zhao, Jie Wu, Liang Li, Zhi Tian, and Weilin Huang. Mogao: An omni foundation model for interleaved multi-modal generation. arXiv preprint arXiv:2505.05472, 2025.
Jie Liu, Gongye Liu, Jiajun Liang, Yangguang Li, Jiaheng Liu, Xintao Wang, Pengfei Wan, Di Zhang, and Wanli Ouyang. Flow-grpo: Training flow matching models via online rl. arXiv preprint arXiv:2505.0544 2025a.
Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang, Honghao Fu, Chunrui Han, Guopeng Li. Yuang Peng, Quan Sun, Jingwei Wu, Yan Cai, Zheng Ge, Ranchen Ming, Lei Xia, Xianfang Zeng, Yibo Zhu, Binxing Jiao, Xiangyu Zhang, Gang Yu, and Daxin Jiang. Steplx-edit: A practical framework for general image editing. arXiv preprint 250: 17761, 2025b.
Shiyu Liu, Yucheng Han, Peng Xing, Fukun Yin, Rui Wang, Wei Cheng, Jiaqi Liao, Yingming Wang, Honghao Fu, Chunrui Han, et al. Steplx-edit: A practical framework for general image editing. arXiv preprint arXiv:2504. 17761, 2025c.
Xiaoran Liu, Zhigeng Liu, Zengfeng Huang, Qipeng Guo, Ziwei He, and Xipeng Qiu. Longllada: Unlocking long context capabilities in diffusion lims. arXiv preprint arXiv:2506. 144.199906..442, 2025d.
14429,
Yuqi Liu, Bohao Peng, Zhisheng Zhong, Zihao Yue, Fanbin Lu, Bei Yu, and Jiaya Jia. Seg- zero: Reasoning-chain guided segmentation via cognitive reinforcement. arXiv preprint arXiv:2503.06520, 2025e.
Rabeeh Karimi Mahabadi, Hamish Ivison, Jaesung Tae, James Henderson, Iz Beltagy. Matthew E Peters, and Arman Cohan. Tess: Text-to-text self-conditioned simplex diffusion. arviv preprint 20X.0:2305.08379. 2023.
Fanqing Meng. Lingxiao Du, Zongkai Liu, Zhixiang Zhou, Quanfeng Lu, Daocheng Fu, Botian Shi, Wenhai Wang, Junjun He, Kaipeng Zhang, et al. Mm-eureka: Exploring visual aha moment with rule-based large-scale reinforcement learning. arXiv preprini arXiv:2503.07366. 2025.
Shen Nie, Fenggi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li. Large language diffusion models. arXiv preprint arXiv:2502.09992 2025.
Yuwei Niu, Munan Ning, Mengren Zheng, Weiyang Jin, Bin Lin, Peng Jin, Jiaqi Liao, Chaoran Feng, Kumpeng Ning, Bin Zhu, et al. Wise: A world knowledge-informe semantic evaluation for text-to-image generation. arXiv preprint arXiv:2503.07265. 2025.
Jingyang Ou, Shen Nie. Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li. and Chongxuan Li. Your absorbing discrete diffusion secretly models the conditional distributions clean data. arXiv preprint 246::24060037366 2024.
13

---

Published as a conference paper at ICLR 2026
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the JEEE/CVF international conference on computer vision, pp. 4195-4205, 2023.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVE confer- ence on computer vision and pultern recognition, pp. 10684-10695, 2022.
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv
preprint arXiv:2010.02502, 2020.
Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. arXiv preprint arXiv:2405.098/8. 2024.
Yinjie Wang, Ling Yang, Bowen Li, Ye Tian, Ke Shen, and Mengdi Wang. Revolutioniz- ing reinforcement learning framework for diffusion large language models. arXiv preprint arXiv:2509.0694 06949, 2025.
Cong Wei, Zheyang Xiong, Weiming Ren, Xeron Du Ge Zhang, and Wenhu Chen. Omniedit: Building image editing generalist models through specialist supervision. In ICLR, 2024.
Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng-ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, et al. Qwen-image technical report. arXiv preprint arXiv:2508.02324, 2025a.
Chenyuan Wu, Pengfei Zheng, Ruiran Yan, Shitao Xiao, Xin Luo, Yueze Wang. Wanli Li, Xiyan Jiang. Yexin Liu, Junjie Zhou, et al. Omnigen2: Exploration to advanced multimodal generation. arXiv preprint 2r06v:2506. 1.../8887) 18871, 2025b.
Yongliang Wu, Zonghui Li, Xinting Hu, Xinyu Ye, Xianfang Zeng, Gang Yu, Wenbo Zhu, Bernt Schiele, Ming-Hsuan Hsuan Yang, and Xu Yang. Kris-bench: Benchmarking next-level intelligent image editing models. arXiv preprint arXiv:2505.16707, 2025c.
Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408. 12528, 2024.
Yi Xin, Qi Qin, Siqi Luo, Kaiwen Zhu, Juncheng Yan, Yan Tai, Jiayi Lei, Yuewen Cao, Keqi Wang, Yibin Wang, al. Lumina-dimoo: An omni diffusion large language model for multi-modal generation and understanding, arXiv preprint arXiv:2510.06306 2025.
Ling Yang. Bohan Zeng, Jiaming Liu, Hong Li, Minghao Xu, Wentao Zhang, and Shuicheng Yan. Editworld: Simulating world dynamics for instruction-follovie image editing. arXiv preprint DrXiv:2405.1478. 2024.
Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, and Mengdi Wang. Mmada: Multimodal large diffusion language models. arXiv preprint arXiv:2505. 15809, 2025a.
Yi Yang, Xiaoxuan He, Hongkun Pan, Xiyan Jiang, Yan Deng, Xingtao Yang. Haoyu Lu, Dacheng Yin. Fengyun Rao, Minfeng Zhu, et al. R1-onevision: Advancing generalized multimodal rea- soning through cross-modal formalization. arXiv preprint arXiv:2503. 2025b.
Jiacheng Yc, Zhihui Xic, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, and Lingpeng Kong. Dream 7b, 2025a. URL https: : /wwkulllp...//// gipp..gtte github..uu........g.. httbi/////////// 2025/ dream.
Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, and Lingpeng Kong. Dream 7b: Diffusion large language models. arXiv preprint arXiv:2508. /5487, 2025b.
Jiasheng Ye, Zaixiang Zheng, Yu Bao, Lihua Qian, and Mingxuan Wang. Dinoiser: Diffused condi- tional sequence learning by manipulating noises. arXiv preprint arXiv:2302.10022 2023.
Zebin You, Shen Nie, Xiaolu Zhang, Jun Hu, Jun Zhou, Zhiwu Lu. Ji-Rong Wen, and Chongxuan Li. Llada-v: Large language diffusion models with visual instruction tuning. arXiv preprint 2oXi:2250516692 2025.
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Published as a conference paper at ICLR 2026
Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng, Agrim Gupta, Xiuye Gu, Alexander G Hauptmann, et al. Language model beats diffusion- tokenizer is key to visual generation. arXiv preprint arxtv:2310.057.7, 2023.
Qifan Yu, Wei Chow, Zhongqi Yue, Kaihang Pan, Yang Wu, Xiaoyang Wan, Juncheng Li, Siliang Tang, Hanwang Zhang, and Yueting Zhuang. Anyedit: Mastering unified high-quality image editing for any idea. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 26125-26135, 2025.
Jingyi Zhang, Jiaxing Huang, Huanjin Yao, Shunyu Liu, Xikun Zhang, Shijian Lu, and Dacheng Tao. R1-vI: Learning to reason with multimodal large language models via step-wise group relative policy optimization. arXiv preprint 2rXiv:2503.1299 2025.
Kai Zhang, Lingbo Mo, Wenhu Chen, Huan Sun, and Yu Su. Magicbrush: A manually annotated dataset for instruction-guudee guuded image editing. Advances in Neural Information Processing Systems, 36:31428-31 44444949444, 2023.
Haozhe Zhao, Xiaojian Shawn Ma, Liang Chen, Shuzheng Si, Rujie Wu, Kaikai An, Peiyu Yu, Minjia Zhang, Qing Li, and Baobao Chang. Ultraedit: Instruction- based fine-grained image editing at scale. Advances in Neural Information Processing Systems, 37:3058-3093, 2024.
Xiangyu Zhao, Peiyuan Zhang, Kexian Tang, Xiaorong Zhu, Hao Li, Wenhao Chai, Zicheng Zhang, Renqiu Xia, Guangtao Zhai, Junchi Yan, et al. Envisioning beyond the pixels: Benchmarking reasoning-infone informeed visual editing. arXiv preprint arXiv:2504. 02826, 2025.
Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Chunwei Wu, Jun Hu, Jun Zhou, Jianfei Chen, Yankai Lin, Ji-Rong Wen, al. Llada 1.5: Variance-reduced preference optimization for large language diffusion models. arXiv preprint 1rxXiv2500..1922, 2025.
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APPENDIX CONTENTS
Scaling MMaDA-Parallel
16
B Additional Results
17
B.1 Qualitative results
17
B.2 Quantitative Results
17
C More Related Work
18
D Preliminaries
20
D.1 Preliminaries of discrete Diffusion Models.
20
D.2 Group Relative Policy Optimization for Discrete Diffusion Models
22
Sampling Details on Text and Image
23
Details of Training Dataset Curation
23
G Details of ParaBench
25
H More Implementation Details
26
I More Ablation Studies
26
J Limitations and Future Work
27
Denoising Demo
29
L Use of LLM
29
M Prompts for evaluation
30
A SCALING OF MMADA-PARALIEL
To further validate our MMaDA-Parallel on larger-scale training, we extend our post-training framework on Lumina-DiMOO Xin et al. (2025). Lumina-DiMOO shares similar architecture with MMaDA, but benefits from much larger-scale data training and substantially stronger visual tokenizer, amused-VO Xin et al. (2025). The original MMaDA tokenizer is known to be ma- jor bottleneck for visual fidelity and text rendering, which restricts the achievable performance of both sequential and parallel paradigms. By switching the amused- VQ tokenizer, the limitations in reconstruction and fine-gra tne-ggrined detail generation are largely removed, allowing to evaluate our parallel framework in a setting where the tokenizer is no longer the dominant constraint. We adopt identical training settings in Lumina-DiMOO, and report its corresponding quantitative and qualitative results in Table and Figure 8. The results clearly show that after applying our Parallel framework and ParaRL post-training. Lumina- Lumina-DiMOO surpasses BaGEL and achieves state- of-the--rt performance in thinking- aware synthesis. This strongly validates both the scalability and the headroom of method once the tokenizer bottleneck is addressed.
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Input
Edit Prompt
Bagel (w/ think)
IMaDA-Paralle
Portray
flower wilting aftter being left in hot sun.
oe saae nokeed and
Illustrate the leaf falling from tree stxong gust.
Reslace the Zaptops datustatic
tallecs with Extepued gionlng aaiag Lapaut
trampanent
transpacest tablets
and change the gloning bise eneagy
to be eacuased sits Kiea.
recogparis diazaays, dhink
Show the vase shattered witth flowors scatttered.
long
ouggast
spidezveb tozn by falliigg
bzanch fron
neazby tzee.
Figure 8: Additional qualitative results on MMaaA-Parallel-A, post-trained from
Table Main results on ParaBench. MMaDA-Parallel-A represents our variant post-trained from
Lumina-DiMae DiMOO.
Model
Text Qual.
Text Align.
Image Cons.
Image Align.
Imuge Qual.
Output Align.
Overall
Open-source models (Non-thinking)
F-De
65.2
77.5
Qwen-Image
84.2
Flux. I-Kontext
77.9
65
84
Qwen-Image- Edit
78.2
73.5
84,1
Bagel (wlo think)
72.2
80.1
Clesed-souree models
GPT-40
92.5
86.2
88.1
Gemini-2.5
94,1
95.2
88.5
76.2
90.2
83.4
$8.9
Open-source models (Thinking-aware))
Bagel (w/ think)
82.0
74.5
76.7
63.4
81.5
52.9
71.8
Show-o* (tuned)
69.1
MMMaDA-Parallet Parallel-A w/o Para-RL
82.6
73.7
71.3
64.6
82.6
63.1
73.0
Purul--PuaallelA Para-RI.
84.1
76.5
71.0
67.8
83.6
68.8
75.3
B ADDITIONAL RESULTS
B. QUALITATIVE RESULTS
We provide more qualitative results in Figure and Figure 10 for thinking-aware image editing and generation.
B.2 QUANTITATIVE RESULTS
We also report additional image-only results of MMaDA-Parallel on RISEBench Wu et al. (2025c)
in Table 7 and GenEyal Ghosh et al. (2023) 8. Along with general image synthesis benchmarks
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Input Image
Prompt
Thinking-Awan Aware Editing with MMaDA-Parallel
quottor sreas gost semcate appoutance To adtoes cusation,
capeet hau Jatee,
Draw what it will look like an hour later.
cacsent voute
segals the boon sasn of the eocle will asanst wire connebacent, souniblly cewending naee of its scanuncee the to the Light eqcauce. acditionally, the stacee cast by twe spole deald guieslly y onteo bingeraset outtoot sulaas okin, detatlad. steecasing sts axtural tastuse the ahadoe tims, ressation esnue hour.. nore are the
ansinet offect,
and ofjecc lig
adpucted will
dading
eslor dergin,
to astono exettie,,
Boorsiigg the inttiel corsider chengeo
aeed atter
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Figure 9: Additional qualitative results on thinking- aware image editing.
WISE Niu et al. (2025) and Gedit-Bench Liu et al. (2025c). For fair comparison, we evaluate against the sequential version of MMaDA, AMMDA--Prallld This variant of MMaDA was trained using the same data and resources as our MMaDA-Parallln achieves consistent performance improvements, demonstrating that
but trained and inferred in sequential manner.
We find that MMaDA-Parrad parallel generation leads to overall gains in image alignment. Compared with the original MMaDA, our approach further narrows the performance gap with Bagel.
C MORE RELATED WORK
Diffusion large language models. Diffusion models have achieved remarkable progress in vi- sion (Ho et al., 2020; Rombach et al., 2022; Esser et al., 2024b; Song et al., 2020; Peebles & Xie, 2023), motivating their extension to text. The discrete nature of textual tokens, however, makes direct adaptation non-trivial. Two main approaches have emerged: learning continuous latent rep- resentations (Chen et al., 2022; Mahabadi et al., 2023; Ye et al., 2023; Gong et al., 2022), and designing discrete diffusion models (Ou et al., 2024; Gong et al., 2024; Liu et al., 2025d; Ye al.,
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Input Prompt
Thinking-Aware Generation with MMaDA-Parallel
A Starbucks coffee cup and bread rolls on a table next to a window with a black car parked outside.
To generate the Lmoge based on the psonpt "A Staztucks coffee cup and bsoad solls on toble noxt to a wandow with a black caz pasked outside" prondnently
fouggoourd the left Place Stadhucss coffee Focition plate with bessd rolls on the zight side of the drane. B Besuze the table moddon and has sazn tonc. 4, Includo wandow in backgrourd cutsise vistole thoough hours to capture ratural light, 7. Ensuze the overall composstion sa talanced with coffee cap and boeed zolls docal puints. this sill gaven pacept.
following
view of stseet.
Mixe
wincow,
diylight
appeniiing smage
A man sitting comfortably on a wooden bench in the midst of tranquil forest glade, encircled by towering trees.
bwith tmage proept
tranquti forwet glate, enciecled toaexing tiocs, I seed Zocus on the following koy clevenss: 1.
*esttiigge: tranauil forest contortably edtrrosphesee: Tesnoudl and secens. 2. = Wooden berch, tall trees, and poacedul
Subjectee;
bonch.
envizoneent, The conposition should place the man as the focal potnt. auttounded should feature tall the bench being contaal slowent.
of toing In a forest glade. The Lighting should be soft and natural. sugpesting a cale, ently somingg ex late afterncon setting. The overall sucthy
tranquil atmouphere.
granuts the laass lunal the ameat chalie, rein heaao,,
reed
vidaccttes Piace thee fostiect tram chaie aeee didis astee dase, and ticlels then aificiat Exoess, potlags will mocage dels in
A trio of chairs, a
trio of banners, trio of desks, a quartet of
stam
allarrattens, sect. contrsating colees, elaces sas the dosk to the coor scss seeee The sostingdamale sanestiondy arbegaabed anto the siiniiks
reftrness tetootee forr
ships, and quartet cushions.
to avaud sem waid shart rwess apeopenate th rslon and bestone for each after thet maite ccrnee scene
night eetilss
To generate irage featuring threr differently coloxed basesall gloves ozzanged on wooden bonch, nould start by cheosing vissant colors for the gloves, zuch as orange, black, and a light taom. Next. mould tecture roslittic woodon
Create an image featuring three differently colored baseball gloves arranged wooden bench.
with natusal zzoin patteen provide the
againet the emcoth arrangeent should shoacase the glams wide by side, 6ith their open calm facing femard, allowing vieners Lighting tectures without creating tarsh stadons. Finalll. the background csuld be misiral, pechaps a bluzeed outenez satting like pask bench, to keep the focus the gloves.
The
the detailed wtstching
peomot plats of parcakes topped with diner setting, + 1 reed to plan the folloning visual eetents: wettinn Subjectee: A glate of pancaless with topoings fbutter. bacon strips, syzu)).
generate the image based
plate of pancakes topped with butter, bacon strips, side of syrup, and coffee cup on a table in a diner setting.
table
cup itcludes tyskcal dincz decor. 4, +LLightings igwe Seft. natural light to wind cozy dires acrosohcze. 5. enfoousittionee Contes tie plate of pascakes : Shew dioer interior with tables, chairs, and posetblly sare pattons io the distante to establ ish the setting. The cvezall goad. to create sarn, snusting. and appetizing irage that
ioume:
next
dince
Figure 10: Additional qualitative results on thinking-awan aware image generation.
Table 7: Overall performance on RISEBench.
Models
Temporal
Causal
Spatial
Logical
Overall
GPT-40-Image
34.1 %
32.2%
37.0%
10.6
28.9%
Gemini-2.0-Flaashee
8.2%
15.5%
23.0%
4.7%
13.3%
Bagel
3.5%
4.4%
9.0%
5.9%
5.8%
MMaDA(Scquentia))
3.9 %
5.2%
8.1%
4.8%
5.5%
MMaDA-Parallel
4.2% 5.5%
8.3%
5.1%
5.75%
2025b; Zhu et al., 2025). Among the latter, Masked Diffusion Models (MDMs) stand out by lever- aging bidirectional attention for global consistency and supporting parallel decoding. Systems such as Dream7B (Ye et al., 2025b) and LLaDA (Nie et al., 2025) achieve performance comparable to autoregressive LLMss. Beyond text, diffusion-based LLMS have also been extended to multimodal
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Table Results on GenEval.
Method
Single Obj.
Two Obj.
Counting
Colors
Position
Color Attri.
Overall
SDXL
0.98
0.74
0.39
0.85
0.15
0.23
0.55
Show-o Xic et al. (2024)
0.95
0.52
0,49
0.82
0.11
0.28
0.53
MMaDA (Yang et al., 2025a)
0,99
0.76
0.61
0.84
0.20
0.37
0.63
Bagel (Deng et al., 2025a)
0.98
0.95
0.84
0.95
0.78
0.77
MMuDA(Scquential) MMaDA-
0.99
0.78
0.66
0.87
0.34
0.37
0.68
0.99
0.83
0.70
0.88
0.40
0.47
0.71
Table 9: Results on WISE
Model
Cultural
Time
Space
Biology
Physics
Chemistry
Overall
SDXL
0.43
0.48
0.47
0.44
0.45
0.27
0.43
Show-o Xie et al. (2024)
0.28
0.36
0.40
0.23
0.33
0.22
0.30
Bagel Deng et al. (2025a)
0.44
0.55
0.68
0.44
0.60
0.39
0.52
MMaDA-Sequential
0.39
0.54
0.58
0.55
0.44
0.22
0.44
MMaaD-Parallll
0.42
0.56
0.59
0.57
0.47
0.24
0.47
domains. LaViDA (Li et al., 2025) employs multi-view image encoding with masked-denoising training. LLaDA-V (You et al., 2025) integrates masked diffusion with visual instruction tuning, and MMaDA (Yang et al., 2025a) unifies reasoning across text and vision generation through chain- of-thought supervision and reinforcement learning. These advances highlight the scalability and versatility of diffusion-based language models across both unimodal and multimodal settings. Nev- ertheless, existing approaches have not yet explored parallel text-image co-generation, leaving cross-modal reasoning and alignment still constrained by sequential pipelines.
Reinforcement learning for multimodal foundation models. Reinforcement Learning (RL) has emerged as powerful paradigm for enhancing reasoning and controllability in large models. The widely adopted GRPO (Guo et al., 2025a) applies rewards primarily on the correctness of the final answer and the adherence to predefined format. Recently, RL has been adopted in multimodal large language models (Chen et al., 2025b; Meng et al., 2025; Yang et al., 2025b; Zhang et al., 2025; Deng al., 2025b; Huang al., 2025b), incorporating task-specific rewards such rectness, intersection-ooer--niooo (IoU) for localization (Liu et al., 2025e), and image-text alignment scores (e.g.. T21-RI (Jiang al., 2025a)). Extensions such (Jiang al., 2025b; Hong al., 2025) further introduce cross-modality coherence rewards. In the context of diffusion language models, similar strategies have been explored with verified rewards and carefully designed probability ap- proximations (Yang et al., 2025a; Gong et al., 2025). Despite these advances, most existing methods focus solely on rewards applied the final output, while largely ignoring the generative trajectory. This overlooks the fact that intermediate steps can provide crucial signals for alignment. In contrast, our work investigates the synergy between modalitics during the denoising process and introduces ParaRL, which exploits stepwise semantic alignment to optimize thinking-awan awar multimodal gener- ation.
D PRELIMINARIES
D. PRELIMINARIES OF DISCRETE DIFFUSION MODELS.
In recent years, diffusion models have set new standards in generative modeling. While De- noising Diffusion Probabilistic Models (DDPMs) excel in continuous domains like raw pixel spaces, Discrete Denoising Diffusson Probabilistic Models (D3PMs) have proven highly effec- tive for discrete data, such as tokenized images and text. Models like VQ-Diffusion Gu et al. (2022), MaskGIT (Chang et al., 2022), Muse (Chang et al., 2023), Show-o (Xie et al., 2024), and MMaDA Yang al. (2025a) have demonstrated that discrete diffusion process can generate high- fidelity outputs with great efficiency. Our model's architecture is built upon this discrete diffusion paradigm. We provide the formal preliminaries, beginning with the foundational forward and
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Table 10: Results on GEdit-Bench
G_SC
G_PQ
G_O
Gemini 2.0
6.73
6.61
6.32
GPT-4o
7.85
7.62
7.53
Instruct-Pix2Pix (Brooks al., 2023)
3.58
5.49
3.68
MagicBrush (Zhang et al., 2023)
4.68
5.66
4.52
AnyEdit (Yu et al., 2025)
3.18
5.82
3.21
StepIX-Edit Liu et al. (2025c)
7.09
6.76
6.70
Bagel Deng et al. (2025a)
7.36
6.83
6.52
MMaDA-Sequential
5.63
5.97
5.13
MMaDA-Parallel
5.72
6.28
5.23
reverse processes and culminating in the simplified mask-and and-predict training objective that our model employs.
Forward and reverse processes. A discrete diffusion model consists of two key processes: (1) The Forward Process (q), fixed Markov chain that gradually corrupts input data Xy over T timesteps into noisy latents xy...., X,: and (2) The Reverse Process (pa), learned neural network that reverses this corruption by progressively denoising
recover the original data distribution. Let's consider single token zo E (1...., K} from a codebook of size K. The forward process at each step is defined by stochastic transition matrix Q E R. K A key property is that the distribution x, conditioned on the initial state Xg tractable:
qxx, Xo) = (x,(x
where Q, = Q1 Q2
..Q...QQ.
Q.
(5)
The posterior probability, which is essential for training, is also tractable:
q(x+-1|x4, xo) g(xe|xx- )q(xx-1 x Cat Xt-1
x xtQ X Q
(6)
q(x, Xo)
Xa Q:x!
where @ denotes element-wise product.
A bsorbing mask state and transition matrix. The design the transition matrix Qr dictates the nature of the corruption. A highly effective approach, inspired by masked language modeling, is to introduce special absorbing [MASK] state. This expands the token vocabulary Once token becomes [MASK), it remains masked for all subsequent timesteps. This explicitly sig- nals corrupted positions to the model. The transition matrix for this "Absorbing-Unile process is defined as:
+ states.
Wi + Ur.
Ot
W: U4.
E R <R(+1)x(K+))
Q2
Vt
1/
Wi
+ 1/
at
where at each step I, token has a probability aa to be masked, a probability 3r to be replaced by random token, and probability wi (1 04 (3) remain unchanged. The [MASK] token (last row) always transitions to itself.
Objective mask prediction. The training objective for diffusion models is derived by maxi- mizing the Evidence Lower Bound (ELBO) on the data likelioo. The negative ELBO, which minimized during training, can be decomposed into several terms representing different stages of
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the diffusion process:
r
CELBO = By log pa p x1)
KL(g(xx-1|xx X
pp (x:- (x,)
Reconstruction Term
Denoising Matching
(8)
(L((((7/<0)ll((x7))
Prior Marching
Here, the objective consists of three main components: (1) a reconstruction term that learns to generate the final data from X1, (2) a series of KL. divergence terms that train the reverse process po to match the true posterior at each denoising step, and (3) a prior matching term that aligns the final noisy latent with simple prior distribution. Following derivations in D3PMs Austin et al. (2021), this complex objective can be simplified to a weighted sum of reconstruction terms:
Ssimle Σ
EE [xxx)xx) log (o (x)]x
When using the absorbing mask state strategy, this simplified objective becomes equivalent Cross-Entropy loss for mask token prediction, used in MaskGIT Chang et al. (2022). This ap- proach is highly effective as focuses the model's capacity on reconstructing only the corrupted parts of the data. Our work leverages this powerful paradigm for both text and image token genera- tion.
D.2 GROUP RELATIVE POLICY OPTIMIZATION FOR DISCRETE DIFFUSION MODELS
Group Relative Policy Optimization (GRPO) (Guo et al., 2025a) is a powerful policy gradient al- gorithm originally designed for autoregressive models. However, direct application to discrete diffusion models is non-trivial. The core challenge lies in computing the importance sampling ratios and sequence-level likelihoods; these are straightforward
an autoregressive chain but ill-defined in non-autooregressive, parallel decoding process. Diffusion models lack sequential history for token-level probabilities, and their policy distributions are implicitly dependent on masking patterns, making direct likelihood estimation computationally prohibitive.
To bridge this gap, we adopt the efficient random masking framework from MMaDA (Yang et al., 2025a) to adapt GRPO for diffusion-based architecture. This strategy circumvents the need for direct likelihood computation by using the model's predictions on randomly masked inputs as an unbiased estimate of the policy likelihoods. First, the advantage A, for each response o, in generated group {o;}5 computed in the standard group-relative manner:
A, = F: - (
(10)
10 (
where r; is the reward for response o;, The policy gradient then calculated using an importance sampling ratio (0) defined over randomly masked version of each response, where unique mask ratio p; ~ UO, 1] is sampled for each response each training step. This allows the standard clipped GRPO objective to adapted for diffusion models as follows:
JDin=-GRO(()) EgwD foc (v.)-0(0011]
Σ
min (T),(0)AA
T
M
FEM.
(11)
-
clip (1) + A.) A.)) DK (='gl ((or)
where the expectation is also taken over the random mask ratios, the inner summation is only over the masked tokens M, and x' denotes the policy likelihoods approximated via the masking scheme. This formulation enables stable and efficient policy optimization by effectively adapting the princi- ples of GRPO non-autoregressive setting.
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E SAMPLING DETAILS ON TEXT AND IMAGE
Parallel sampling and denoising strategy. Our model employs a parallel sampling strategy, pre- dicting logits for all text and image tokens simultaneously in a single forward pass. The denois- ing process for both modalities is guided by a confidence- based re-masking schedule, inspired by MaskGIT (Chang et al., 2022) and LLaDA (Nie et al., 2025). Crucially, while the logits are gen- erated jointly, we apply distinct masking schedulers and confidence metrics to the text and image tokens to account for their different statistical properties and generation requirements.
Image token denoising. For image generation, follow the iterative decoding process from MaskGIT. At each timestep 6, given the current set of M masked image tokens, the model predicts For each masked position we sample candidate token vi from the predicted probability distribution and compute its confidence score 84- A mask scheduling function (L/TT) determines the number of tokens m [r(t/T) that should be kept (i.e., remain unmasked). We select the m tokens with the highest confidence scores to keep for the next step t + 1, and the remaining M m tokens are re-masked. The update rule for a token at position i is:
logits / = (e) )
{ Uoo if 8; < sorted, [m))
((+1)
(12)
Lu otherwise
where u, represents the [MASK] token and (s,)ed)(s))(m) is the m-th value in the sorted list of confidence scores. This iterative refinement continues until all image tokens are finalized. In our implementation, we generate 512px image, which is encoded into 1024 discrete tokens and takes 30 steps to decode.
Text token denoising. For text generation, we adopt the semi-autoregrresivve denoising strategy from LLaDA (Nie et al., 2025), where the output sequence is generated in blocks from left right. Within each block, however, generation is non-autoregressive and iterative. The core of this process a reverse sampling step that transforms partially masked sequence x, step i into less masked sequence x, at an earlier step s < t. This transition is formally characterized by the probability:
x + [M] , x x/.
N-1
x [M] , x [M]
(x)e(xx/x)) (x,) = II qu (x and (x)(+//x)))
T-o, T-ar pe (2t (x);
x M A [M]
otherwise,
(13)
where (=(x x2) is the model's prediction of the original token for the masked position and at =
1 1-t. t. In practice, this involves an iterative refinement loop. At each step, given the current sequence X,, we first sample candidate tokens for all masked positions. Then, following the deterministic low- confidence re-masking strategy adopted by LLaDA, we identify the tokens with the lowest prediction confidence scores and re-mask them for the next refinement iteration.
In our implementation, we generate the sequence with 256 sequence length, in blocks of 64 tokens and 128 steps. At each denoising step within a block, we unmask the two tokens with the low- confidence scores. This block-based, semi-autoregrressive approach is essential for generating coherent and naturally structured sentences, as it mitigates issues like the premature generation of end-of- se-uunce (IBOS) 1) tokens that can arise in a fully non auo-eetoreeressivv setting.
F DETAILS OF TRAINING DATASET CURATION
Our training dataset is a carefully curated collection of 150,000 high-quality samples designed for thinking-awand -ware image synthesis. The primary challenge was that existing public datasets for image editing and generation typically provide input-output pairs without the intermediate reasoning traces required by our method. Therefore, our curation process involved three main stages: (1) aggregating data from ofate-of-theear sources, (2) generating high-quality reasoning traces to augment this data, and (3) applying a rigorous filtering and enhancement pipeline. The final dataset consists of 100,000 editing pairs and 50,000 generation pairs, achieving a 2:1 ratio. An overview of the dataset is shown
in Figure 11 and 12
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Figure 11: Overview of our dataset for thinking-awann editing
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Figure 12: Overview of our dataset for thinking-aware editing
Source datasets for editing data. We constructed the 100,000 thinking- aware editing pairs by sourcing from four diverse and challenging benchmarks:
HQ-Edit (Hui et al., 2024): This dataset provides nigh-resolution images with wide variety of detailed editing instructions, serving as a source of high-quality visual content for our training.
UltraEdit (Zhao et al., 2024): We leverage UltraEdit for its collection of complex editing instructions that require strong reasoning and compositional abilities, pushing the model beyond simple object manipulation.
AdyEdit (Yu et al., 2025); Given the vast size of AnyEdit, we selectively sampled from its more challenging categories. Specifically, we focused on the impl ieppiicittom wew.iiiiiin sub- set, which contains instructions that do not explicitly mention the target object, requiring the model to infer the user's 's intent.
Edit (Yang et al., 2024): This dataset is crucial for its focus on edits that require world knowledge and complex reasoning, such as causal (e.g. "what if storm occurs") and temporal (e.g.. "What's 's this man like in twenty years?") edits. To further bolster our model's capabilities in these areas, we performed data augmentation this subset, using GPT-4o to generate three times the amount of similar, complex reasoning-baad based instruc- tions and corresponding edits.
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Source dataset for generation Data. For the 50,000 thinking-awart generation pairs, sourced data from ShareGPT4o (Chen et al., 2025a). This dataset contains a rich collection of diverse, real-world prompts and corresponding high-quality image outputs, providing strong foundation for pprross,, knowledge-intensive image synthesis.
Reasoning trace generation. A core step in our curation process was to augment the source data with reasoning traces. Since the original datasets only provide triplets of ('input image', 'instruc- tion', "output image"), we utilized the powerful multimodal model Qwen2.5-VL-7B (Bai et al., 2025) to generate plausible reasoning text for each sample. The model was prompted with the input/output image pair and the instruction to produce a step-by-step rationale explaining the trans- formation. This transformed our dataset into quadruplets: ('input image', 'instruction', 'reasoning trace", "output image"), which is the required format for our thinking-aware training.
Data filtering and quality control. Finally, to ensure the highest quality, we applied multi- stage filtering pipeline to the entire s50ppe dataset. First, we removed near-duplicates to increase data diversity. Second, we used a scoring mechanism based on Qwen-VL to identify and discard samples with low-quality or visually unappealing images. For cases where the instruction was valuable but the image quality was poor, we leveraged GPT--4o to regenerate nigher-fidelity candidate images. This comprehensive curation process resulted in a clean, diverse, and high-qualing dataset optimized for our training objectives.
G DETAILS OF PARABENCH
ParaBench is a comprehensive benchmark designed to address the limitations of cxisting evaluation protocols for thinking-awand image synthesis. Unlike traditional benchmarks that focus solely on the final image, ParaBench is built to assess the entire generation process, including the quality of the intermediate reasoning trace and its synergy with the visual output. It comprises a total of 300 challenging prompts, curated from various sources and divided into 200 for editing and 100 for generation.
Composition of editing prompts. The 200 editing prompts are meticulously curated and synthe- sized from various existing benchmarks to test wide spectrum of complex reasoning abilities. To provide a structured analysis, we group them into five distinct categories:
Spatial Reasoning (40 prompts): These are tasks requiring a deep understanding of object locations, orientations, and spatial relationships. Examples include instructions like "place the book to the left of the lamp" or "make the person in the background larger."
Temporal Reasoning (40 prompts): These prompts involve reasoning about time and require the model infer past future states. Examples include "show what this street might look like 50 years from now" or "revert the shattered vase to its original state."
Causal Reasoning (40 prompts): This category contains instructions that require the model to infer and depict cause-and-effect relationships. Examples include "show the ground after a heavy rain" or "make the plants look like they haven't been watered for weeks."
World Knowledge (40 prompts): These are edits that require external, real-world knowl- edge to execute correctly. Examples include instructions like "turn this car into model from the 1980s" or "edit the painting be in the style of Van Gogh."
General Editing (40 prompts): This category includes a broad set of common, founda- tional editing operations that do not fit into the specialized categories above. It primarily consists of instructions for adding, removing, or replacing objects and serves baseline for fundamental editing capabilities.
Composition of generation prompts. The 100 generation prompts are sourced from the ShareGPT4o (Chen et al., 2025a) dataset. They designed to be open-ended and cover wide range of scenarios, including the generation of creative scenes, complex compositions with multiple interacting objects, and images that require interpreting long, descriptive narratives.
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Evaluation axes. All 300 prompts in ParaBench evaluated using LLM-as-a-judge frame- work across six fine -ennegd axes to provide a holistic assessment of a model's 's performance. The evaluation criteria are follows:
Text Quality: Assesses the fluency, coherence, and grammatical correctness of the gener- ated reasoning text.
Text Alignment: Measures how well the reasoning text follows the user's input instruction and accurately plans the edit/generation.
Image Quality: Evaluates the photorealism, aesthetic quality, and absence of visual arti- facts in the generated image.
Image Alignment:
Measures how faithfully the generated image adheres to the user's
instruction.
Image Consistency (for editing tasks): Assesses how well the model preserves the unedited parts of the original image, maintaining background, style, and object identity.
Output Alignment: Evaluates the cross-modee consistency between the generated reason- ing text and the final generated image.
We provide the prompts for thinking-aware image editing in Appendix M. The prompts for image generation follow the same format, with only minor modifications in the input and representation style.
H MORE IMPLEMENTATION DETAILS
Training details. Our model is initialized from the weights of MMaDD--MxXCTT (Yang et al., 2025a), which utilizes LLaDA-8B its text backbone and MagVIT-v2 for image tokenization. The post-training process consists of two stages. In the first stage. we perform supervised finetuning (SFT) for 30,000 steps on our curated dataset of 150,000 thinking-aware samples. In the second stage, we conduct Parallel Reinforcement Learning (ParaRL) for 10,000 steps, using challeng- ing subset of approximately 15,000 examples (10%) drawn from the SFT dataset. Both training stages were conducted on 32 NVIDIA A100 GPUs with global batch size of 768. We utilized the Adam W optimizer with a learning rate of 2e-5 and a cosine learning rate schedule with a warm-up of 500 steps. We drop 10% of text input and 10% of image input to support classifier-free guidance sampling.
In ParaRL, we randomly sample s 3 trajectory points. The steps of these certain points identical in the same rollout and uniformly sampled in all rollouts. We set KL constraints 3 0.0001 keep the same with MMaDA's baseline.
Inference details. During inference, our model employs a parallel sampling strategy, generating the logits for all text and image tokens simultaneously in a single forward pass. The images generated with classifier-fre guidance scale of 3.5, and text with scale of O.
I MORE ABLATION STUDIES
Any-Order generation We further conducted ablations on any-order generation methods. this setting, we adopt an identical linear scheduler for both text and image denoising, matching their training configuration. The resulting samples are shown Figure 13.
As illustrated, applying any-order generation leads to noticeable degradation in both textual and visual quality. On the text side, the model exhibits insufficient semantic understanding: fails to articulate the specific form of "creature from folklore." On the image side, instruction following becomes weaker: the model inaccurately places the scene "by the riverbank" directly on top of the riverbank, and the rendered creature is very normal and not "from folklore". Quantitative results on ParaBench in Table 11 further demonstrate that modality-speciff schedulers provide stronger thinking aware image synthesis performance.
We further analyze three key design choices of our framework: (1) modality awaar reweighting in the training objective, and (2) the decoding strategy (parallel semi-parallel vs sequential).
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Input
vintago car paskeed by the rivezbank.
fictional creature from folklore.
txansforn
To cantbur the cumtion trape the axtuett imape aad viraaee
suacioo sairaliiiin the last on Erteed the, fheee
reetbark Hind
sdnetly dry Remomass
Add Tearloration fup the restrsc lon with mithiol preates rect av cars iepoeeee
leproand
The a aments ae voeden dock the can the uureet wines
Dihanss Appasamos
The velign can shiil plined new work, riieehs the
seee
1 be pas sand tuat appear as parhad mestcoodd anth quartinn
pose terkuru loney haren, mesd faom ownrm
lightng
draponiiae
Any-Ordex Scheduler
The shoul now clinki eeiis perhip free the med zoo8 exmtur, to plowent
laritage eyet, deil tic wou anda mane
Shepe
1 cibakierool kome hesciggts wnhane
Bedy Adast the logi be relunt, mesorar with miceer tock linge
uprigh
en endhigh
the plaued sigidy
Honer Beal Sevce the heneh more mone
The overe berra pould moseaan the sencns somoushen
uncei eeprosi
Detal::
"Ea
tranefoms quertinn image into answer lamed to aud
Iottondore the arer the first smay unsuul auroore costure or Ever fridbe dowrteed and Tranaformation:
car porked the ixcrbank. Hore's reasoning
idendily the Kay Elements: The day slements in the queeton image are the set, The main changes are the adittion the virtage cor and the compostion of the scene
iicce the dock, the catin, and
sujject Traufkrmatin The mntmaal image is as actioss the
Vintage Car: vintage car should be placed right of design, pertaps comp
oline os cac is wir uo pootlly a hore minar su hoad of th diggis
mody Franeeeraaion Thenthreador:
elongated
astalle couctann
Modal-Speciffi Scheduler
has been ridi and lon frong the rheir It should be clsse cnough the
Festioning: The chourd te Honed a that aopporss it
Hertaite design
pau
cabin out not too cooee it, mainaninn the pesiefall amibiaace of scene.
lightng and Shadows: the lihting should nemain cona stant with the editing gragge, mainmmni ng the soft glow from the surset.
Postane more porture rightyy and
ling til, yai byy firvo reperssii
command prasend.
The lighting should
warm hass
stronphere
posbbl th miginn risna on the drago's
adding there details, the scene will with quantion
ereeaae antpessii surse hevire cing husker the natural sarting
Figure 13: Comparisons with any-order generation
Table 11: Ablation on any-order generation
ParaRL 8
Text Qual.
Text Align.
Image Cons.
Image Align.
Image Qual.
Output Align.
Overall
onder generation Modal-Specifie
73,2
64.2 71.0
70.3
57.4
$2.6
66.4
80.4
73.4
63.2
81.2
59.8
71.5
Modality reweighting. Table 12 shows that using (tea(t) (t) = 1/t and Wing (t) 1 stabilizes image training and yields the best overall performance. Applying the same schedule to both modalities either destabilizes training (both 1/t) reduces alignment (both constant).
Decoding strategy. Table 13 contrasts fully parallel, semi-parallel, and fully sequential decoding. In the sequential variant, text is generated autoregressively and then used as the sole conditioning signal for image generation, which makes the output vulnerable to error propagation across modalí- ties. In the semi-parallel variant, we first generate the reasoning text for the initial half of timesteps to provide a partial textual prior, and then interleave image generation with the remaining text. This strategy mitigates some sequential errors and yields improvements over the fully sequential baseline. Finally, the fully parallel variant, i.c., MilaA--Paraalld generates text and image jointly at every denoising step. We find that fully parallel decoding achieves strong results without requiring exten- sive textual priors, likely because the early image steps can already establish coarse scene layouts, and excessive initial text may even bias attention toward irrelevant details.
J LIMITATIONS AND FUTURE WORK
Failure cases In Figure 14, we present several representative failure cases of our model. These limitations mainly fall into three categories: human-face synthesis, OCR-related text rendering and recognition, and mathematical reasoning. The first two types of failures are largely inherited from the discrete visual tokenizer (MagVIT-v2 used by both method and the MMMDA baseline. This tokenizer struggles with reconstructing high-fidelity human faces and fine-grained textual details, which naturally constrains the model's ability to generate accurate facial structures coherent text.
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Table 12: Ablation on modality reweighting.
Table 13: Ablation on decoding strategy. Fully parallel is our default.
Default uses (t) (t)==/t, Wing 1
Setting
Text Align.
Image Align.
Output Aligu.
Strategy
Text Aligu.
Align.
Output Align.
Both
69.5
56.3
Sequential image)
56.5
Both
63.7
61.9
57.0
Sem--garalel (grouped)
Whex 1/f,
71
63.2
59.8
Fully partilll (ours)
71
63.2
Input Image
Prompt
Thinking- Aware Editing with MMaDA-Paxallel
image rewerr sicinngtee the boll Heee's the nestering
To Torrande
conddar scnsario
what if the boy kicks the
thy's detiors ument hir motion, monning
bon slanding supporingg be kidked
onject's imgues The the and thou id vid and in mvnsoon indizaiin thst been dotedd This can
the slowengtt spuce to tichol whroret whding trar should
Appeorance
tho
dyro with
ball
ars caledd sugascingg hos just dicked hall it cheid couldion lightyy sane dilo ceftectingeh the
saades offece: There dooud
dinn
betinnilty suggessin
has been sicted
Reckgrondd The bnckgronnd nome armite but ah medt crmphiiise boyy
By apppiing cha ior, the ree at mage will clea depics boy blcking bail
manns cuentin image roser iranga. mand to foc lowding stepp:
change the word "you" in this
"ou' and "L" bechgrune shone urbsr setting with cityigrs
Fooa on Tias The goall change word fira
TIJ-OIKE WE
Moentt tecton that reads "sour the sigs.
Replice nox "You" with on the some digs
Martie the cartent
sign into
Goop rast badiground and jysllss, and city lights) as original
The new "we" shoue be en tog
buckgro and ensurig stames tocal
poirt of image.
"we"
Draw
transform suestion imagee arswer imagpe, need add cangent
inage
han dlitiim but need intindaee paint where the fundioo intreaiing.. common choice would be draw vartical seat curs the the niree-
ideatify Elements The
showing distant view
of the trees
Add sont line. horizontala shape can be added to rearesent the slopp of the fanctionn It as
Highight bottom curee, ercaring highert value.
sane angle, farther away.
shorp
Since question aloaads mentions
point by drowing
top
make fanction
sttdd thow smoothheess the prominent. This acriio crvieved nereas contreat betwoees the rure and baniggnund snow.
Highightt carve lighting
maiing the funcs
Essure Visibility The ighning should aduusted to enarre the now line is clearl viubll detinct, entereing umasl impart imape.
Figure 14: Failure cases.
For mathematical reasoning, our training corpus primarily focuses on general editing and genera- tion tasks, without incorporating math-oriented reasoning datasets. As result, the model exhibits weaker logical consistency and reduced visual execution quality math-heavy scenarios. We be- lieve that replacing the tokenizer with continuous more advanced discrete visual representations could substantially mitigate issues related to faces and OCR, and that integrating recently emerging multimodal mathematical reasoning datasets holds promise for improving performance in math- related tasks. We leave these directions for future work.
Limitations Although our approach achieves notable improvements, several limitations remain. First, our base model MMaDA is trained on relatively limited data, which constrains its fundamen- tal capabilities. As result, it is difficult to consistently surpass large-scale models such Bagel that benefit from substantially larger training corpora. Second, our current sampling and training strate- gies not yet fully unified across modalities, and exploring more integrated interaction paradigms may further enhance performance.
Future Work For future work, plan extend our paradigm to broader scenarios, such as story generation and multimodal outputs that combine text and images, which we believe will further demonstrate the potential of parallel thinking-aware generation.
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K
DENOISING DEMO
We here provide demo for our parallel thinking-aware image editing in Figure 15. In this demo, text generation adopts a fully diffusion, non semi-ar paradigm.
Denoising Progress 12. 5%
Denoising Progress 25.
Denoising Progress 37. 5%
Denoising Progress 50. 0%
Denoising Progress 62.5%
Denoising Progress 75. 0%
roadle code ung oeleed van monk mndy
Denoising Progress 87.5%
Denoising Progress 100%
Figure 15: Denoising demo, text generation in any order diffusion.
L
USE OF LLM
We employed large language models, specifically Gemini 2.5 Pro and ChatGPT-5, to assist in fining paragraphs and performing grammar checks throughout the writing process. The typical use cases arose in the analysis and discussion parts of the manuscript, where precise and well-struetured
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expression is critical. The models not involved in idea generation, experimental design, data analysis; rather, they served as writing aids to enhance readability and presentation quality.
M PROMPTS FOR EVALUATION
Output Alignment Score Evaluation
Generation of Image Reasoning Following Scores:
You are professional digital artist and image evaluation specialist.
You will be given:
1. **Input Image**; the original image.
2. **Output Image**: the generated/edited image.
3. **Output Text**; the thinkinng/reasoning text that describes the intended result or modification process.
Your Objective:
Your task is to **evaluate how well the output image aligns with the descriptions, reasoning, or expectations outlined in the. output text (thinking)**, Focus on whether the visual content matches what is described or implied in the thinking text
#tt Reasoning:
You must follow these reasoning steps before scoring:
**1. Extract Key Descriptions** What visual elements, changes, or characteristics are described or implied the output text?
**2, Visual Analysis**; What you actually observe the output image? Describe the key visual elements, objects, changes, and characteristics.
**3. Alignment Check**; Compare the descriptions
**]** with visual observations from **2**; Do the visual elements match what's described the thinking text?
the described changes characteristics actually present in the image?
reasoning or process described
reflected the visual result?
+4. Decision**; Use 1-5 scale assign final
## Evaluation Scale
5):
You will assign **output_ cetpuut_alggnnettsecntee score** with following rule:
**5 Perfect Alignment**; The output image perfectly matches all descriptions and expectations in the output text.
**4 Minor Mismatch**; The image largely aligns with the text, but minor detail differs from the description.
**3 Partial Alignment**: The main elements described present, but there are noticeable discrepancies missing aspects.
**2 Major Mismatch**: Several key elements described in the text are missing incorrectly represented in the image.
**1 No Alignment**; The image does not match the descriptions in the output text or contradicts the stated reasoning.
#t! Guidance:
Pay attention to both explicit descriptions and implied visual outcomes in the output text.
Consider whether the thinking process described is reflected in the visual result. If the output text describes specific objects, colors, positions, or changes, check if these are accurately represented.
If the text explains reasoning for certain visual choices, evaluate whether those choices are evident the image.
Output Format
Pravide the evaluation score and explanation the following JSON format:
{{
"output alignment :corrt: X,
"reasoning": "1. Extract Key Descriptions: 2. Visual Analysis: Alignment
Check: 4. Decision:
}}
Figure 16: Output alignment evaluation prompt
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Text Quality Score Evaluation
Generation of Text Reasoning Quality Scores:
You are professional multimodal reasoning and evaluation specialist.
You will be given:
**Inpu Text**: reasoning prompt describing how to generate edit an image.
## Objective:
Your task is to **evaluate the quality of the reasoning prompt"*, focusing on: **Clarity**; whether the instructions are clearly expressed and unanbiguous **Compltteness*: whether key details necessary for correct image sulting/generationn are included
**Conslstencyt*; whether the reasoning flow is logically connected and free from contradictions
**Relevance**; whether the text focuses on the image editing task rather than irrelevant details
**Conciseness* whether the reasoning avoids redundancy and unnecessary verbosity
*u Evaluation Scale (1 to 5):
**5 Excellent Quality**: Instructions are clear, complete, logically consistent, concise. No ambiguity.
Minor Issues**; Mostly clear, with only small redundancies or slightly missing details, but task remains well defined.
Noticeable Flaws**; Some ambiguous phrasing, partial omissions, or unnecessary verbosity that may confuse interpretation.
Significant Issues*;; Multiple contradictions, missing steps, unclear instructions that risk incorrect or incoherent image editing. Poor Quality**; Completely unclear, contradictory, irrelevant image task.
Guidance:
Check the following aspects and mark them / (satisfactory) (problematic):
*Clarity**; Clear, unambiguous instructions
Completeness** Includes all essential details for the task *Consistency** Logical steep reasoning, contradictions
Reeevvnnc Focused on the image generation/eddting task *Conciseness**: Free from redundancy and unnecessary verbosity *Accuracy**; Descriptions align with the intended visual changes
/ The more checks, the higher the score.
## Output Format:
After evaluation, provide your score and concise reasoning using the following JSON format: "ssoon
"text quwlltyysscoreeee X, "reasoning": "Clarity: v/x, Completeness: //x, Consistency: v/x, Relevance: //X, Conciseness: V/X, Accuracy: V/X. [Brief explanation of key issues or strengths]"
Figure 17: Text quality evaluation prompt
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Text Alignment Score Evaluation
Generation of Text Alignment Scores:
You professional multimodal reasoning evaluation specialist. You will evaluate the alignment between **input image**, an **input text instruction"t, and **AI- generated reasoning text**.
You will be given:
1. **Input Image**: the original image before editing generation.
2. **Input Text Instruction** the intended modification generation request.
3. **Output Reasoning Text**; the step-by-step reasoning produced by the model.
=u Objective:
Your task is to *tevaluate how well the output reasoning text aligns with both the input instruction and the input image**, focusing on whether the reasoning correctly interprets the request and remains faithful to the visual content. You must:
"*Identify the core visual and textual requirements"* from the input image
instruction.
**Check whether the reasoning text explicitly and correctly reflects these requirements.
**Not penalize stylistic differences**, only misalignment, hallucination, or omission. **Be ; reasoning may mention edits unrelated to the instruction or inconsistent with the input image, which should reduce the score.
Reasoning:
You must follow these steps before scoring:
*+1. Instruction Understanding** Summarize the main requirement(s) from the input text instruction.
**2, Image Context**; Identify relevant details from the input image that instruction refers (e.g., objects, attributes, positions).
the output reasoning text proposes (step-by- *43. Reasoning Analysiş**;; Summarize
step actions, described changes).
**4. Alignment Check**; Compare (1)+(2) with (3):
Does the reasoning focus the correct object(s) attributes in the image?
Does correctly interpret the requested change(s)?
Are all requested aspects addressed (not omitted contradicted)?
Does it avoid introducing unrelated hallucinated edits supported the image/Enstruction
**5, Decision**; Use the 1-5 scale to assign final score.
## Evaluation Scale to 5):
You will assign an **text **text_alignmenn aww.amentt_n__eeeeeeeemmee score** with the following rule:
**5 Perfect Alignment**; Reasoning fully and faithfully reflects both the image and instruction, with no omissions or hallucinations.
**4 Minor Issueş*#; Reasoning captures the main intent but slightlly misses visual detail or minor nuance.
**3 Partial Alignment**: Reasoning covers the main idea but has noticeable onissions, inaccuracies, or weak grounding in the image.
**2 Major Misalignment Reasoning only weakly relates to the instruction or image; key aspects are missing or wrong.
**1 "oon-Aligmmen**: Reasoning ignores or contradicts both the instruction and the input image.
нu Output Format: Provide your evaluation in the following JSON format:
"sJson
"text alignment score": X, "reasoning": "1. Instruction Understanding: ... 2. Image Context:
3. Reasoning
Analysis: ... 4. Alignment Check: ... 5. Decision: ..."
Figure 18: Text alignment evaluation prompt
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Image Consistency Score Evaluation
Generation of Consistency Scores:
You are professional digital artist and image evaluation specialist.
You will be given:
1. **Input Image**: the original image.
2. **Output Image**: the generated/edited image.
3. **Input Text**; the instruction describing the intended modification.
Your Objective:
Your task is to **evaluate the visual consistency between the input and output images, focusing exclusively on elements that are NOT specified for change in the input text instruction**, That is, you should only consider whether all non-instructed details remain unchanged. Do **not** penalize or reward any changes that are explicitly required by the instruction.
## Evaluation Scale (1 to 5):
You will assign a "*consistecy_ssee serrrcy_scre"*t according to the following rules: **5 Perfect Consistencys*; All non om-instruction elements are completely unchanged and visually identical.
Minar Inconsistency Only one very small, non-instruction detail different (e.g-, a tiny accessory, subtle shadow, or minor background artifact).
Noticeable Inconsistency** One clear non-instruction element changed (e.g., different hairstyle, a shifted object, or visible background alteration). or more non-instruction elements have been
**2 Significant Inconsistency**: noticeably altered.
major non-instruction details are different (e-g., changed identity, gender, or overall scene layout).
Severe Inconsistencyt*;
Guidance:
First, **identify all elements that input instruction explicitly allows requires changed**, Exclude these from your consistency check.
For all other elements (e.g., facial features, clothing, background, object positions, colors, lighting,
composition, etc.), **compare the output image to the input image** and check if they remain visually identical.
If you observe any change in non-instruction element, note it and consider its impact the score.
If the instruction is vague anbiguous, make best-effort factual inference about which elements are intended to change, and treat all others non-instruction elements.
## Note:
**Do not penalize changes that are required the instruction.** **
**Do not reward penalize the quality or correctness of the instructed change itself** (that is evaluated separately).
If the output image introduces new artifacts, objects, or changes to non-instruction elements, this should lower the consistency score.
#tl Output Format
First, clearly explain your comparison pracess: list each major non-instruction element and state whether it is consistent (unchanged) or inconsistent (changed), with brief reasoning.
Then, provide your evaluation in the following J5ON format: {[
"reasoning": "Compared to input image, [list non-instruction elements that changed or remained the same] in the output image.", "'onsistency_scoree: X
}}
Figure 19: Image consistency evaluation prompt
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Image Quality Score Evaluation
Generation of Quality Scores:
You are professional digital artist and image evaluation specialist.
You will be given:
**Output Image**: AI AI-generated image.
## Objective:
Your task is to **evaluate the perceptual quality** of the output image, focusing on: **Structural and semantic coherence**
*Natural appearance**
*bssence of generation artifacts**
*tvisual clarity and composition**
You must *not penalize law resolution or moderate softness** unless it introduces semantic ambiguity or visually degrading effects.
## Evaluation Scale (1 to 5): You will assign a **quality_aa sqquality_score*** score** with the following rule:
**5 Excellent Quality**: All aspects are visually coherent, natural, free from noticeable artifacts. Structure, layout, and textures are accurate and consistent. The image has clear composition and professional appearance.
Minor Issues**: One small imperfection (e.g., slight texture blending, minor lighting inconsistency, small compositional flaw).
***3 Noticeable Artifacts**; One or two clear visual flaws or semantic problens (e.g., fingers, minor duplication, slight distortion, unnatural lighting).
Structural Degradation**: Multiple distracting errors (e.g., melted hands, warped shapes, unreadable text, poor composition, obvious artifacts).
**1 Severe Errors**: Major structural fallures hallucinations (e.g., broken anatomy, distortions, completely unnatural appearance).
garbled symbols,
Guidance:
Check the following visual aspects and mark them V (satisfactory) (problematic): "*Structural coherence**; Correct anatomy, object shapes, legible text, proper proportions
**Natural appearance**; Realistic lighting, perspective, shadow logic, believable
textures
*Artifact--reeet No duplication, ghosting, watermarks, obvious generation artifacts
Texture fidelity**; Clothing, hair, surfaces not melted corrupted
*Composition"" Clear focal points, balanced elements, appropriate framing
*Color harmony**: Natural color relationships, appropriate saturation and contrast
/ The more checks, the higher the score.
*u Output Format:
After evaluation, provide your score and concise reasoning using the following JSON format:
{{
"quality_score"" X, "reasoning": "Structural coherence: J/x, Natural appearance: v/X, Artifacts: v/x,
Texture fidelity: v/X, Composition: //X, Color harmony: V/X. [Brief explanation of key issues or strengths]" }}
Figure 20: Image quality evaluation prompt
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Image Alignment Score Evaluation
Generation of Image Instruction Following Scores:
You are professional digital artist and image evaluation specialist. You will evaluate the effectiveness of the AI-generated image based on given rules.
You will be given:
1. **Input Image**: the original image.
2. **Output Image**; the generated/edited image.
3. **Input Text**; the instruction describing the intended modification.
Your Objective:
Your task is to **evaluate how the output image faithfully fulfills the input text instruction**, focusing **exclusively on the presence and correctness of the specified changes**,
You must:
**Identify detailed visual differences** between Input Image and Output Image
*correclyy and faithfully**,
Determine those differences **match exactly what the input text instruction requests**
**oot assess any unintended nodifications beyond the instruction""; such evaluations under separate criteria.
"*Be careful**, an edit may introduce visual change without fulfilling the actual instruction (e.g., replacing the object instead modifying it)
Reasoning:
You must follow these reasoning steps before scoring:
*#1. Detect Difference**; what has visually changed between Input Image Output Image?
(e-g., size, shape, color, position) this step, you don't to information from the input text instruction.
**2. Expected Visual Caption**: write factual description of the output image perfectly followed.
should look the instruction
**3. Instruction Match**:
Compare the observed differences in **1** the expected change in **2**;
Was the correct object modified (not replaced)?
Was the requested attribute (e.g., size, color, position) modified intended?
Is the degree of modification accurate (e.g., "match size," "slightly increase," " etc.)? **4. Decision**; Use the 1-5 scale to assign final score.
#t Evaluation Scale You will assign an *iinsrucctio__ccom "core** with following rule:
5):
**5 Perfect Compliance* The output image *"precieely matches** the intended modification; all required changes are present and accurate.
**4 Minor Omission**: The core change is made, but *tainor detail** is missing or slightly incorrect.
**3 Partial Compliance*s; The main idea is present, but one or more required aspects are wrong or incomplete.
**2 Major Omission**: Most of the required changes are missing or poorly implemented.
*+1 ioncc:mppiancee* The instruction is **not followed at all** or is **copletell misinterpreted**
нu Output Format
Look at the input again, provide the evaluation score and the explanation in the following J5ON format:
{{
"instruction score": X, "reasoning": "1. Detect Difference: ... 2. Expected Visual Caption: 3. Instruction
Match: 4. Decision: ..."
}}
Figure 21: Image alignment evaluation prompt
35


Reviews:
[{"review_id": "f3RXJq6eSI", "reviewer": "Reviewer_rn82", "summary": "Based on the observation that existing AR models can suffer from error propagation during their thinking trajectory, this paper 1) proposes a benchmarking focusing on text image quality and output alignment, 2) develops a discrete-diffusion-based approach for parallel denoising of both text and images, and 3) introduces parallel RL to further optimize the intermediate cross-modal synergy.", "questions": "1. Please see Weaknesses.\n\n2. In Table 3 (Table 6, 7 as well), why is MMaDA-Parallel decoding compared with the sequential decoding baseline that generates text before images? Why is it not compared to any-order joint decoding?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "The paper is well structured and clearly motivated. It offers a thorough investigation that includes new benchmarking, curated training datasets, new model design, and an RL protocol aimed at addressing the problem. The proposed MMaDA-Parallel model performs on par with SOTA open-source model that is trained on more data.", "weaknesses": "Normally, the decoding process only has one scheduler. In this paper, two schedulers are used for each modality. Could the authors give a more systematic guarantee of why we can assume the independence between each modality and why the alignment of text image modality would enable independent, parallel generation to work better than any-order joint generation?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wmq4V3n6WP", "reviewer": "Reviewer_WaTC", "summary": "This paper focuses on an important problem in image generation, that the inclusion of reasoning can reduce the semantic fidelity of the generated images. It identifies that this could be caused by the misalignment of the model’s generated reasoning and its final image. To solve this problem, the authors propose bidirectional attention between modalities at every denoising step, and optimize alignment along the denoising trajectory. Experimental results show that the proposed method achieves significant improvement over baseline methods.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed ParaBench is an effective tool for the analysis of thinking-aware image synthesis.  The finding regarding the strong correlation between performance degradation and poor alignment between the generated modalities is interesting and insightful. \n2. This paper explains the method design clearly, with insightful motivations. \n3. Visualization results in Figure 5 are impressive, showing the improvement in challenging scenarios such as the compositional settings.\n4. The paper is well-written and easy to follow.", "weaknesses": "1. This paper shows the improvements of the paper based on the proposed ParaBench (Table 2). How about standard public benchmarks used in the original Bagel paper, such as  GenEval[1], WISE[2], GEdit-Bench[3]?\n\n[1]  Geneval: An object-focused framework for evaluating text-to-image alignment. NeurIPS, 2023.\n\n[2] Wise: A world knowledge-informed semantic evaluation for text-to-image generation. arXiv preprint arXiv:2503.07265, 2025.\n\n[3] Step1x-edit: A practical framework for general image editing. arXiv preprint arXiv:2504.17761, 2025.\n\n2.  Figure 5 shows some results of the proposed method.  What is the failure case of the proposed method?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OaVdPHKSVW", "reviewer": "Reviewer_nFAx", "summary": "This paper investigates the failure modes of thinking-aware generation and identifies that sequential reasoning–generation pipelines often suffer from error propagation. To systematically analyze this issue, the authors introduce ParaBench, a new benchmark that jointly evaluates reasoning and image outputs. Building upon this insight, they propose MMaDA-Parallel, a diffusion-based framework enabling continuous text–image interaction, and further optimize it via Parallel Reinforcement Learning (ParaRL). Experiments on ParaBench show a 6.9% improvement in output alignment over Bagel, demonstrating enhanced cross-modal consistency.", "questions": "see the weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.The paper provides a clear and insightful analysis of failure modes in thinking-aware multimodal generation, highlighting a real problem in current reasoning–generation pipelines.\n\n2.ParaBench offers a valuable evaluation framework that jointly measures reasoning and image alignment, which could be useful for future multimodal research.\n\n3.The proposed parallel diffusion framework with stepwise semantic optimization is well-motivated and achieves measurable gains in cross-modal alignment.", "weaknesses": "1. The method novelty is limited — the proposed MMaDA-Parallel and ParaRL mainly combine existing ideas of diffusion fine-tuning and reinforcement learning under a parallel setting.\n\n2.The reported 6.9% improvement in output alignment, while positive, appears modest given the additional complexity and training cost. The authors are encouraged to provide stronger evidence that this gain is statistically or practically significant, or to further improve the results through more comprehensive experiments.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DPWlGri4JL", "reviewer": "Reviewer_w4zL", "summary": "This paper addresses a critical and often overlooked failure mode in ​thinking-aware image synthesis, where the incorporation of a Chain-of-Thought (CoT) reasoning step can paradoxically degrade the final output due to error propagation in sequential, autoregressive pipelines. The authors systematically identify that this degradation is strongly correlated with poor alignment between the generated reasoning text and the final image.\n\nTo tackle this fundamental issue, the paper introduces a novel ​parallel multimodal diffusion framework​ that enables continuous, bidirectional interaction between text and images throughout the entire denoising trajectory. This paradigm shift from sequential to parallel generation allows for real-time grounding of textual descriptions in visual evidence and vice versa, effectively mitigating the accumulation of errors.", "questions": "1. In Figure 3, the denoising process for text is depicted as sequential sentence-by-sentence generation. However, in inference, the denoising of text spans should occur in an arbitrary, non-sequential order. Could the authors clarify this, otherwise the diagram and method might cause confusion.\n\n2. In the Appendix experiments, which model does \"MMaDA-sequential\" refer to? Is it the \"MMaDA-MixCoT\"? It appears that \"MMaDA-parallel\" only slightly outperforms \"MMaDA-sequential.\" If \"MMaDA-sequential\" is fine-tuned using the proposed constructed data, would there be further performance improvements?\n\n3. The generated CoT text relies on \"Qwen2.5-VL-7B.\" How is the high quality of the constructed text ensured? Why wasn't a larger model, such as a 72B version, used for labeling? If higher-quality reasoning texts were constructed, could this further enhance performance?\n\n4. In parallel reinforcement learning, is using \"Qwen-VL\" to evaluate the semantic alignment between partial text and partially generated images reliable? Since intermediate generated images might still be blurry or meaningless, the VLM's preference may not effectively judge meaningful scores.\n\n5. Although MMaDA-Parallel achieves higher output alignment on ParaBench compared to other open-source models like Bagel, its output image quality does not surpass them. In other words, how can the author prove that output alignment is positively correlated with generating high-quality target images?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces a paradigm shift by proposing a parallel multimodal diffusion framework to replace sequential autoregressive pipelines for thinking-aware generation. This creative combination of discrete diffusion with bidirectional cross-modal attention effectively addresses the critical failure mode of error propagation identified in existing models. \n\n2. The proposed ParaRL algorithm, which applies reinforcement learning along the denoising trajectory, further represents a significant conceptual advance in optimizing for intermediate alignment.\n\n3. The paper is exceptionally well-written and structured, with a clear narrative that logically progresses from problem identification to solution and validation.\n\n4. It establishes a more robust paradigm for thinking-aware image synthesis, with potential broad impact on the field of multimodal generation. The introduction of the ParaBench benchmark fills a critical gap by providing a diagnostic tool to evaluate cross-modal alignment, which will be important.", "weaknesses": "Please see my questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)