Janus-Pro-R1: Advancing Collaborative Visual Comprehension and Generation via Reinforcement Learning
Authors: Kaihang Pan
Yang Wu
Wendong Bu
Kai Shen
Juncheng Li
Yingting Wang
liyunfei
Siliang Tang
Jun Xiao
Fei Wu
ZhaoHang
Yueting Zhuang
Venue: NeurIPS 2025 poster ()
Keywords: Image generation
Image understanding
Primary area: applications

Abstract: Recent endeavors in Multimodal Large Language Models (MLLMs) aim to unify visual comprehension and generation. However, these two capabilities remain largely independent, as if they are two separate functions encapsulated within the same model. Consequently, visual comprehension does not enhance visual generation, and the reasoning mechanisms of LLMs have not been fully integrated to revolutionize image generation. In this paper, we propose to enable the collaborative co-evolution of visual comprehension and generation, advancing image generation into an iterative introspective process. We introduce a two-stage training approach: supervised fine-tuning teaches the MLLM with the foundational ability to generate genuine CoT for visual generation, while reinforcement learning activates its full potential via an exploration-exploitation trade-off. Ultimately, we unlock the Aha moment in visual generation, advancing MLLMs from text-to-image tasks to unified image generation. Extensive experiments demonstrate that our model not only excels in text-to-image generation and image editing, but also functions as a superior image semantic evaluator with enhanced visual comprehension capabilities. Project Page: \url{https://janus-pro-r1.github.io}.

Janus-Pro-R1: Advancing Collaborative Visual Comprehension and Generation via Reinforcement Learning
Kaihang Pant->, Yang Wu?.*, Wendong Bu²-*, Kai Shen Juncheng Li', Yingting Wang",
Yunfei Li,, Siliang Tang li,, Jun Xiao', Fei Wu', Hang Zhao?, Yueting Zhuang'
Zhejiang University', Ant Group²
(kaihangpan, wendongbu, shenkai, junchengli, siliang) edu. on
Abstract
Recent endeavors in Multimodal Large Language Models (MLLMs) aim unify vi- sual comprehension and generation. However, these two capabilities remain largely independent, as they are two separate functions encapsulated within the same model. Consequently, visual comprehension does not enhance visual generation, and the reasoning mechanisms of LLMs have not been fully integrated revo- lutionize image generation. In this paper, propose to enable the collaborative co-evolution of visual comprehension and generation, advancing image generation into an iterative introspective process. We introduce two-stage training approach: supervised fine-tuning teaches the MLLM with the foundational ability to generate genuine CoT for visual generation, while reinforcement learning activates its full potential via an ixplloation-eeploitaaaii moment in visual generation, advancing MLLMs from text-to-ima toagie tasks to uni- fied image generation. Extensive experiments demonstrate that our model not only excels in text-to-ine ooxt-to-image generation and image editing, but also functions supe- rior image semantic evaluator with enhanced visual comprehension capabilities. Project Page: https://jaant: jatp:////aauuss-rrrrrrr proo-rroorr. github.io
trade-off. Ultimately, we unlock the Aha
Introduction
Recently, multimodal large language models (MLLMs) 5 12 have emerged to unify comprehension and generation across various modalities within the same next-token prediction paradigm of large language models (LLMs) 33 Specifically, given user query about comprehension- "What kind of dog is in this picture [LMG]", or generation- "Generate an image of a cute cat", the model can complete the task by sequentially predicting the appropriate text or image tokens.
However, for current MILMs, visual comprehension and generation remain largely independent rather than forming synergistic relationship. integrated into single model seemingly just for the sake of avoiding parameter redundancy [5] (4, Especially in visual generation tasks, augo-regression- based methods are intended to unleash the powerful reasoning capabilities of MLLMs to infer more semantic-aligne aliignd images more complex contexts. Unfortunately, even state-of-the-art MILMs like Janus-Pro [5]. still fall short of user expectations for basic text-to-image generation, also limited to accepting only pure text input for generation. This highlights that the robust reasoning mechanisms of L. have not been fully integrated to revolutionize, or even advance visual generation. Thus,
Equal Contribution.
Project Leader.
Corresponding Author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Genuine Chain-of- Thought Thaighof-Thought
Unlocking Aho Moments
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Generation
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Figure I:
We could bring about three revolutionary benefits for image generation after collaborating the visual comprehension and generation capabilities with MLLMs.
key question naturally arises: Is it feasible to synergize visual comprehension and generation, incorporating the reasoning mechanisms into visual generation? Once such collaboration is achieved, the MILM can seamlessly combine and switch between its comprehension and generation capabilities, bringing about three revolutionary benefits for image generation, as shown in Figure
1) Genuine
Thought (CoT) [54]: A genuine CoT in MLLMs should be self-driven by the model's deep thinking within a unified next-token prediction framework based on the causal dependency of tokens. The visual comprehension and generation capabilities are naturally linked to form an interleaved text-image reasoning chain under the spontaneous scheduling of the MLLM, which can be treated as a CoT that truly helps produce more refined images.
2) Unlocking Aha Moments [16]: Gemuine self-driven CoT further endows MLLMs with the ability of self-correction, unlocking the Aha Moments, After generating the initial image, the MLLM leverages its comprehension capability to reflect on the current generation. Once errors are detected, it re-engages its visual generation capabilities to re- re-produce images that better meet user requirements.
The emergence of the above two benefits signifies that the model can effectively collaborate its visual comprehension and generative abilities. This not only en- hances its performance in text-to-image tasks, but also enables flexible unified image generation [56) for any complex situational purposes, such as image editing.
3) Enabling Unified Image Generation:
Furthermore, the collaboration between visual comprehension and generation should yield mutual benefits. This means that as visual comprehension evolves the capabilities of visual generation, it also enhances its own performance in the process.
Motivated by the above goals, we develop Janus-Pro-RI, advancing text-to-image generation into an introspective process and taking step toward unified image generation with the collaborative co-evolution of visual comprehension and generation capabilities. The visual generation capability anchors the lower bound, ensuring the model can produce appropriate images. Meanwhile, the visual comprehension capability elevates the upper bound, enabling robust reasoning chains that unlock Aha Moments in image generation.
Specifically, our training process comprises two stages. In the first stage, we employ supervised fine-tuning (SFT) to endow MLLMs with the foundational ability to construct a genuine reasoning chain for visual generation that triggers Aha moments. To achieve this, we break down the CoT generation into several sub-skills with a mixed training approach, upgrading text-to-image generation into an iterative introspection mechanism. After each round of generation, the MLLM self-reflects on whether the generated image meets the requirements and repeats the generation if it does not. The model's 's spontaneous self-reflection during this process forms genuine CoT for visual generation, triggering Aha Moments where the model redirects its reasoning back onto the correct path.
However, training solely based on SFT tends naively mimic the training distribution rather than performing true reasoning. Therefore, in the second stage, we treat image generation as long token-level Markov decision process and perform reinforcement learning (RL) based on GRPO algorithm 45 Without any ground-truth images, we encourage the model to spontaneously collaborate its comprehension and generation capabilities for introspective eext-to-image generation, also designing a bi-level QA-based reward function for optimization. It represents a trade-off between exploration and exploitation [48]: we avoid explicitly teaching the model how to solve the problem but encourage autonomous exploration, while we still provide it with appropriate incentives to facilitate effective exploitation. And we find that RI. enables the MILMM to autonomously develop advanced CoT for image generation, evolving from initial imitation to genuine reasoning.

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Thanks to the above training strategy, with Janus-Pro as the backbone, Camus-Pro-Rl achieves stronger text-to-image performance on varioss T2I benchmarks with the collaboration between the visual comprehension and generation capabilities. More remarkably, the capability synergy endows the MLLMM with the potential for unified image generation tasks (e.g., image editing) and further enhances its role as excellent image semantic evaluator, bolstering its visual comprehension capabilities. Our main contributions are threefold:
We introduce a two-stage training paradigm, collaborating the visual comprehension and generation capabilities within the MLLM to construct genuine chain-of-thought and unlock the aha moments for text-to-image generation.
We further extend text-to-image generation to the scenarios of image editing, unleashing the potential for unified image generation.
Our model achieves superior performance on both text-to-image generation and image editing tasks, also possessing enhanced image comprehension capabilities.
2
Related Work
Unified Visual Comprehension and Generation. Recent studies focus on unifying visual compre-
hension and generation within unified MLLM. Some efforts [2 47 (50] cascade external diffusion
models after the output of MLLMs for visual generation, while some approaches [5] 39
64
52
35
tokenize images into discrete space and then conducte unified AR for text and vision. Others 58 63
try to integrate the objective of AR and diffusion into single model. However, visual comprehen- sion and generation still remain largely independent without collaborative synergy, and the strong reasoning mechanism of LLMs fails to revolutionize visual generation. In this paper, we aim to promote collaboration between these two capabilities, enabling a genuine CoT for visual generation and unlocking its Aha moments.
CoT in Visual Generation. Recently, some studies have attempted to introduce CoT into visual generation. [17] considers the CoT as intermediate images within the DDPM process. [IO] proposes combination of two models: MLLM for reasoning chain generation while the diffusion model interprets the CoT for image generation. Moreover, although [53] and the concurrent work [2I] integrate the CoT and image generation within single model, the CoT is more akin forced textual planning of the organization logic for the target image. This represents a rudimentary form of thinking, failing to genuinely drive the MLLM's deep reasoning and introspection for image generation. We argue that a true CoT should emerge spontaneously from the model's ' deep thinking, naturally collaborating its visual comprehension and generation capabilities into interleaved image-text reasoning chain, which could unlock the Aha moment in visual generation.
3 Method
In this section, we introduce how to collaborate visual comprehension and generation abilities to achieve introspective image generation with genuine CoT that unlocks the Aha moments. We outline two training stages (Figure 2): supervised fine-tuning ($ 3.1) and reinforcement learning ($ 3.2
Finally, we further endow MLLMs with advanced capabilities unified image generation ($ 3.3
3.1 Supervised Fine- Tine-Tuning
Similar to teaching complex tasks in everyday scenarios, during the SFT phase, we break down the Chain of visual generation reasoning into several subtasks with mixed training approach. By doing so, the MLLM acquires preliminary ability to continuously imitate specific subtasks based on the current context, finally exploring coherent reasoning chain for visual generation.
Data Preparation. We aim to construct an image-text collection =(P; [[I] (/),
as the supervised training data, where each prompt P corresponds to M images I. For each image- text pair, there an associated semantic consistency score E [0. 1] and detailed reason R for semantic matching (S > 0.5) non-matching (S < 0.5), where 5 > 51
S.M.

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Stage 1: Supervised Fine-Tuning
Stage 2: Reinforcement Learning
Task-/
Usagge
Task-I1
Matchesl vol
Task-ill
Smage
Captions:
cute
Reward
Comp.
Raward
Match
LLM
LLM
LLM
Input
based
VQA.
1tl
Wlsngtch
Reward
................................
...................
Boxt Tekemuzr
Iwage Pcc
Text Fol
Iaag
Fest Tat
Irat Leage
Outpur
Nutch
O..... 040
Mcrkeed
Rismatch
9+1
Generation
Self-evaluation
Image Regeneration
Gradient Optimization
040
[806]
[BOX]
Text tokema
BD
Trage Takerd DMDD
Figure 2: We include two training stages to unlock aha moments with CoT in visual generation: supervised fine-tuning and reinforcement learning.
Specifically, we first leverage the Qwen model 1 to develop a total of N = 200, 000 prompts. For each prompt, employ the FLUU 23 & Janus-Pro 5 for text-to-image generation, and then utilize the Intern
model 6 to evaluate whether the generated image and text are semantically consistent. We record the probability answer "Yes" as PYes and "No" PNo. with consistency score S = PYe/ (Pss + Pso). More details are shown in Appendix B
Mixed Training. We first decompose the process of visual generation CoT, which elicits the aha moment, into three steps and designed three basic sub-tasks:
Task-I: Texa-to-Image Generation: In C, we select text- pairs with S 2 0.8 for rext-to-image
training. The objective function p(y²) = Σe= j=1 log Poly,lues, P), where y² are tokens
ground-truth image with sequence length S²
Task-II: Self-evaluation of Text- Consistency: We aim enhance the capability of MLLM to determine whether given images are semantically consistent with the text, also providing the rationale for its judgment. To construct training data, for each prompt we first select 1) images (I', Rd 1-= fj=1 with < 0.5 as the preceding context (t 2 1). Subsequently, we randomly select one positive image (8 2 0.7) and negative image (S < 0.5), respectively, the target image I, to be evaluated. The objective function is p(yT T') E log Po WVT,, (I* .No, R* )= where T is (Yes, RD) for positive images and (No, R') for negative images.
We aim to enhance the MILLM to correct previous errors and regenerate accurate images. For each prompt, we first select t images {I, R, with < 0.5 as the preceding context to simulate previous incorrect generations and corresponding self-reflections. Then we randomly select an image with S 2 0.8 as the ground- t-th regenerated image. The
Task-Ill: Image Regeneration:
objective function
*() = log (1 ++ 1 P. (I* No, R* 1)
j=1
Through the above supervised mixed training, the MILM learns integrate different subtasks for introspective text-to-image generation, developing a reasoning chain of deep thinking. Ultimately, acquires the fundamental capability to trigger Aha moments.
3.2 Reinforcement Learning
The RL phase aims to effectively balance the exploration trade-off to unlock the full potential of the MLLM. While encouraging the model autonomously explore reasoning pathways, we also provide appropriate incentives for both its process of visual generation and comprehension as the exploitation, advancing it from mere imitation to genuine reasoning.
Bi-Level QA-based Rewards. The overall design philosophy of our reward model is leverage QA-based visual comprehension models (i.e., Intern VL2.5-26B), which will return consistency score R (.) [0, 1] for each eext-image pair, to assess the accuracy of the generated image and the MLLM's self-evaluation. And we provide incentives for the final output images along with their preceding reasoning chains, incorporating bi-level reward scores: visual generation reward Gen and visual comprehension reward Comp. Assume that we permit the MILMM to perform up to maximum of T round image generations and the MILMM determines that has correctly generated the image in the K -th round (resulting in total of K images K Je T > K), we have:

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(1) The generation reward R = ER (T (T- + x RRA(I*). The K K-th image assigned a potentially larger weight because it represents the final output with higher importance.
(2) The comprehension reward RComp Σ (I) -E(()) XT/K, where SE(Z) = O or 1 is the self- aelfsassessment on whether the generated image is semantically aligned with the text.
Policy Gradient. We leverage Group Relative Policy Optimization (GRPO) 45 as the training algorithm, which has been proven to be highly effective for exploring the reasoning capability of the LLMs. The preliminary of GRPO is given in Appendix [ Specifically, given the input prompt P. we prompt the old policy oold to first sample group of G individual initial images {I, G
and obtain the first-round generation reward for each generated image {R Jiz1 (R9A Jizl
Subsequently, the MLLM will assess the semantic consistency between [ Ji=1 and P. returning
the evaluation results as (SE ) Based on the self evaluaatln results, we further obtain the (R[emp)C
first-round comprehension rewards [R.11 Ji=1 {1 |R SE(T))
If the MILM self-assesses that any generated image not semantically aligned, it will initiate the next round for image regeneration and re-evaluation. Otherwise, it will directly output the image generated in the current round. Assuming that we conduct a maximum of 'T' rounds in a group with
the i-th sample undergoing K2 rounds, the final reward can be calculated as R = R RGen R
Comp
(E (_n=1 K,-1 R + (T K,) x R ((T))) + شーー
R2, C T/K; Then we can obtain the advantages
{ where each A measures the relative quality of output compared to the average reward:
A R. mean (B))
(I)
std (R. )2-))
Finally, we upgrade the policy network parameters by the following training loss:
]
[()) E
clip min (lissA,,,lipppas 1-e, 1+5 A.) - -3DKL (TT (TTol (TTet)
(P.a)D (v)) (m)) (-1P)
M
i=1
(2)
where DEL the KL divergence to maintain training stability, and Pt.j is the ratio between the probabilities of no and To.. for outputting the current token:
No (V PI No, Ro
output the image in t-th round
To (Vn.j
P, P,yiies)
Then ( mu(u7) luT P. P, No. No 1 ZD
No. No.R
Ps. =
P, Vi,<s)
self-check the image in t-th round
Tood (Ml 107,ey P. (I) No, xX)
3.3 Towards Unified Image Generation: From T2I Generation to Image Editing
Through the above two-stage training, we introduce true CoT into visual generation, evolving the vanilla text-to-image generation into an iterative introspective process. Essentially, it is the result of the collaboration between visual comprehension and generation, which is a necessary condition for advanced image generation tasks, such as image editing. Similar to the process of introspective image generation, image editing also requires understanding of the instructions how to modify existing images and subsequently generate a new image. And it additionally necessitates preserving image fidelity, iee., maintaining the unedited areas in their original state.
Therefore, we only need to further teach existing models for detail preserving to easily achieve image editing capabilities. We also leverage two-stage training for image editing: SFT and RL. During the SFT phase, we fine-tune the MLLM using a small number of high-quality data to learn the basic requirements of the image editing task, with the objective function denoted as: plyZ")) ES" log Po(y, I", pins), where Ie, pins Tout are the input image, the editing instruction, and the edited image, respectively.
During the RL phase, we only provide the image condition and the editing instructions without any ground-truth images. We still leverage V1.2.55...522611 the reward model and design two QA-based rewards: (1) Following score R!w [0, measures whether the model accurately follows
5

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Table 1: Comparison with state-of- of-thhe-rtt models GenEval, T2I CompBench and DPG-Bench zero-sho text-to-image generation. The best results are in bold fonts with the second best underlined.
GenEval
121-CompBench
DPG-Bench
Overallf
SingOhy
TwoOhj
Counting
Colort
Pos.
ColorAtr
Calorf
Shuge?
Testure?
Aug!
Diglusion based Method
PixArt-lllhu
0.98
0144
0.08
68.9
55.8
DALLER
0.96
0.47
0.43
83.50
SD3 g
0.74
0.99
0.94
0.72
0.89
0.35
0.60
84.08
HLUX.1 1el
24
0.66
0.98
0.79
0.73
0.77
0.22
0.45
83.79
Janus-Flov
0.63
0.97
0.59
0.45
0.83
0.53
0.42
80.09
MuLli-buued Method
Show-o 1X
0.68
0.98
0.31
0.50
56.0
41.0
-6.00
67.48
SHED-X
0.49
0.96
0.57
0.29
0.82
0.14
0.15
65.7
49.2
60.3
Fmmn 12
0.54
0.98
0.71
0..34
0.81
0.17
0.21
61.1
47.3
61.88
80.60
DDT-LLaMs
0.66
0.99
0.64
0.56
0.87
0.39
0,48
72.8
51.4
64.2
80.90
VARGPIv1.1
Infinity
0.73
0.85
0.49
0.57
83.16
Janus-Pu Pro 5
0.30
0.99
0.89
0.59
0.90
0.79
0.66
63.6
35.3
49.4
84.17
GPT-4o 34
0.85
0.99
0.92
0.85
0.91
0.75
0.66
SlowwooPAAM 17
0.69
0.97
0.75
0.660
0.83
0.54
0.33
75.0
36.0
66.0
MINT [3)
0.73
0.98
0.82
0.66
0.79
0.55
0,56
0.67
T21-R1
81.3
58.5
72.4
Ours (Wao.Aha)
0.3
0.99
0.93
0.60
0.89
0.82
0.74
81.2
56.3
73.3
85.02
Ours (with Aha)
0.86
0.99
0.56
0.92
0.57
0.78
83.4
39.4
75.2
85.57
the editing request; (2) Preserving score R E [0, 1] indicates how well the model preserves details that are not intended to be changed. And the final reward calculated as Redit 0.5 * flw Reey
For policy gradient, we also adopt the GRPO algorithm. Given each pair of image condition and editing instruction, we sample group of G edited images {I Ja=1 from the old policy. For the i-th image response, we obtain its reward and follow [q.(]p to obtain its advantage A,. Finally the
objective function the same as Eq. 2). More details are given in Appendix B
Experiments
We employ Janus-Pro- as the backbone, developing Kanus-Pro-Rl and Lanus-Pro-RI-BE excelling in lext-to-image generation ($ 4.1). image editing ($ 4.2). and image semantic evaluation 4.3
More details are given in Appendix and
4.1 Introspective Text-to-Image Generation
Automated Metric Evaluation. We first conduct an automated metric evaluation on 3 text-to-image benchmarks: GenEval [14], 121-CompBench [20], and DPG-Bench [19]. The comparison results against both diffusion-baat based and MLLM-based methods, as well as methods incorporating CoT into image generation, are presented in Table D We have the following observations:
(1) In most settings, our model surpasses other diffusion- based and MLLM-based baselines, achieving SOTA performance. For example, for GenEval, the overall performance of Janus-Pro-B even outperforms GPT-4o. This highlights that our model facilitates better vision-text alignment via unlocking the aha moment. (2) Compared to baselines also proposing to incorporate CoT into text-to-image generation, our method achieves superior performance, e.g., consistently outperforming concurrent work T2I-R1 on T2i-Compbench. This highlights the effectiveness of our approach in activating the true CoT for visual generation. (3) Compared to the backbone model Janus- Pro-7B, our method achieves performance improvements of 7.5% on Geneval, 47.0% on T2i- 12i-Compbench, and 1.7% on DPG-bench, respectively, which underscores the effectiveness of our approach. (4) Without activating the Aha moments and directly outputting the initial generated image, performance on the three benchmarks drops significantly, while remains higher than that of Lanus-Pro-7B which indicates that the introspection mechanism effectively improves the image quality and the training also enhances the first-round image generation.
Qualitative Examples. In TigureBante Figure we present qualitative examples of lanus-Pro-RI to trigger Aha moments within its reasoning chains generate superior images. The model could leverage its visual comprehension capabilities to accurately identify the issues its initial-generated images, then unleash the visual generation capabilities to output more accurate image. Even if

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Table 2: Main results on PlE-Bench for image editing task.
Methed
T2I
Structure
Background
Preservation
CLIP Similarity
Model
PSNR
LPIPS
SSIM
InstructPix2Pix [
SDL. [33]
107.43
16.00
271.33
392.22
68.39
23.49
22.20
MagicBrush [1
SD1.5
26.81
26.85
66.67
171.11
83.37
23.89
20.84
InstructDiflosion
SD1.5
74.21
20.88
142.35
353.45
76.70
24.06
21.57
MGIE [
SD1.5
67.41
21.20
142.25
295.11
77.52
24.28
21.79
Seed.X-Eal
EdRAR 33
LlamaCien 16
39.43
21.32
117.15
130.27
75.13
24.87
21.87
Janus- Pro-Edit
Janus-Pro
131.76
Janos-Pro-RI Pro-RI-ERRdI R-Ede (Ours)
Janus-Pro-R R1
35.87
22.81
114.96
123.30
76.80
24.78
22.31
Nice! the coption, showing yellow motorcycle.
Wait! think the image inconsistntt with the coption. There not on oblong frome the image. will regenerate new one.
globe obleng frome, which meets user's reguirements.
photo of yellow bird and black motorcycle
spherical snow globe and oblong frame
Waitt not see the in the image and the image inconsistent with the prompt. I will regerenate now one.
Waift There the natorcycle, will regenerate
Nize The image consistent with the prompt, see photo of rie right of motorcycie.
right motorcycle
Want! image not prompt. braccoli image mill regererate
Waitt The parking neter is not chove the broccolt instood. they oppeor to be monged together: I will regenerate new one.
prompt. the parking
photo parking
Walt think the wonding the stop sign is nather slurry. will regenerate new one.
Watt! think the image inconsistent with the coption becouse the stop sign is white. will reginenate now
Nice' I think good image with brown graffe and white stop sign.
STOP
>
>
>
STOP
photo of brown giraffe
stop sign
Figure 3:
Qualitative examples of introspective text-to-image generation that triggers Aha moments.
the newly generated image still fails meet the requirements, the model can trigger second Aha moment, re-evaluating the issues and repeating the visual generation to produce fully compliant image.
4.2 Image Editing
Automated Metric Evaluation. We use PIE-Bench [22 as the evaluation benchmark for image editing. We compare with instruction-barn based large-scale training methods, with structure distance and background preservation metrics reflecting fidelity preservation, while CLIP similarity is used for editability evaluation. As shown in Table 2] most existing methods fail to achieve good balance between fidelity and editability. While EditAR, which employs the AR framework, demonstrate relatively better trade-off. Compared to these baselines, Sonus-Pro-A R-Edit achieves better overall performance and maintains a good balance between fidelity and editability. Especially compared with EditAR, it excels in most metrics with superior performance.
Qualitative Examples. We also conduct qualitative comparison with both open-source [61 62
and closed-source 34 leading works on multi-turn editing in Figure 4 Our model supports a wide range of editing operations, with the output images consistently resembling the source images while remaining coherent with the instructions. This stable trade-off between fidelity and editability is rarely achieved in open-source models, and in some cases, our results even outperform closed-source models such as Gemini-2. (49].
4.3 Janus-Pro-RI as a Stronger Image Semantic Evaluator
After two-stage training, we find that Janus-Pro-RI also emerges as an excellent image semantic evaluator. Specifically, it can: (1) Assess whether a given image-text pair is consistent, serving as an evaluation function for text-to-image benchmarking: (2) act as a reward model for text-to-image RL. in place of visual comprehension models, enhancing the performance of other text-to-image models.
7

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Change the colic Change the calor of Add stranberry
Adi Hlubberiis coke
Replase blacberries with condies
Rersvve the coke.
Change into sketch style
shope square
the left vase to red nest to coko.
Ours
GPT-4o
Gemini
-2.0
Magic
Brush
Ultra
Edit
Figure 4: Our model achieve a stable trade-off between fidelity and editability in multi-tum turti-turm editing,
Evaluation Metric for Text-to-Iman Image Bench- marking. We first instruct a text-to-image model Janus-Pro-IB to generate over 2, 000 im- ages for GenEval, and use the original object- focused framework within benchmark as the standard for text-to-image alignment evaluation. Then we leverage Janus-Pro Pro-7B, Janus-Pre Pro-SFT (7B, only undergoing the first-stage SFT training without RL), and Intern Pro-R1 (7B) the evaluation functions, paring their results with those of the standard evaluation framework. As shown in Table 3 Janus- Pro-R1 achieves an 81.1 % consistency ratio with the standard framework's assessment. In contrast, Janus-Pro-7B, Janus-Pro-SFT--11 and Intern VL2.5-8B consistently exhibit lower consistency ratios compared to -rous-Pro-RI.
Table 3: The consistency ratio with the standard assessment GenEval, and the reason reliability score evaluated by GPT-4o, when utilizing differ- models for text-image alignment evaluation.
Consistency Ratio on GenFival (S)
Reason Reliability
Method
Score by GPT-4o
Janus-Pro-7B 7B
72.3
76.9
Janus-Pro-SFT (7B)
72.7
78.8
InterVL2..5 (8B)
79.0
and Janus-
Janus-Pro-KI (7B)
81.1
91.1
Furthermore, we collect several hun- dred pairs of semantically mis- matched text-image pairs. We require
before
Jenus Reward InterVi. 88
the above four models to determine whether the semantics of the image and text matched and to provide relevant reasons. We then use GPT-4o the evaluation model score the reasons provided by the four models from 0 (unreasonable) to 1 (reason- able). As shown in Table 3 Janus- Pro-R1 outperforms both Janus-Pro- 7B and Janus-Pro-SFT in providing reasons that deemed more reliable by GPT-4.. It even approaches the performance of the comprehension-onje MLLM, i.e., Intern VL.25- 8B. These results indicate that (1) compared to the backbone model and even comprehension-ome MLLM, Janus-Pro- more closely aligns with the standard metrics within GenEval, achieving greater evaluation precision; and (2) the visual comprehension capability of our model signifi- cantly enhanced after reinforcement learning. In contrast, SFT only endows the MILM with the basic capability for combining visual comprehension and generation, resulting only incremental improvements to its inherent multimodal understanding ability.
Gentiel Overall 300
RI Rasaard Janus-Pa-811775
0830
82.8
15
0.90
0.30
6.00
0718
0725
Janus-1.88
JanusFrr-78
5 0.5
5 0.8
Threshold Solecting ages
Figure 5: (a) RL Performance with different reward models. (b) Performance with different selecting thresholds in SFT.

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Table 4: Zero-shot text-to-image performance GenEval of different models for in-depth analysis
Method
Overall:
SingObi
TwoObji
Counting
Colort
Pos.
ColorAitr 1
Janus-Pro (78)
0.80
0.99
0.89
0.59
0.90
0.79
0.06
Janus. Pro-SPT (2B, wlo aha)
0.81
0.98
3
Janus-Pro-SPT (7B, with alo)
0.81
0.99
0.87
0.57
0.90
0.81
0.72
Janus- Pro-R1 (7B, wlo ala)
0.83
0.99
0.93
0.60
0.89
0.82
0.74
Prous-Pro-RI// (78. with ohhil
0.86
0.99
0.94
0.66
0.92
0.87
0.78
6
Janus Pro-SFFll (78, lask-, wlo aho)
0.79
0.98
0.58
0.36
0.89
0.76
0.65
Janus- Pro-SPT2 (7B, Task-lfy Bark-Ili, with aho)
0.76
0.98
0.86
0.55
0.85
0.73
0.61
Janus-Pro Pro (78)
0.73
0.98
0.82
0.51
0.89
0.65
0.56
9 Janus-Pre Pro-RI (18, w/o alo)
0.70
0.95
0.78
0.53
0.82
0.60
0.52
10 Janus- Pro-R1 (IB, with aha)
0.71
0.98
0.80
0.51
0.84
0.59
0.55
Reward Model for Text-to-Image RL. With Janus-Pro-7B and Janus-1.3B
[55]
backbone
models, we employ Lunus-Pro-7B. Intern [6] and Vanus-Pro-R as reward models to provide text-image consistency scores as the incentives for text-to-image RL. As shown in Figure a). using Janus-Pro-RI as the reward model significantly enhances the overall performance of both backbone models on GenEval following reinforcement learning, outperforming the results with Intern VL2.5-88 as the reward model. The results highlight the superior visual comprehension capability of our model, which can more accurately assess the text-image semantic consistency.
4.4 In-Depth Analysis
Introspective Text-to- Text-to-Image Enables Better Image Editing. To further demonstrate that introspec- tive text-to-image generation with the collaboration between visual comprehension and generation, could enable better image editing, we directly apply the same training paradigm described in $3.3 to Janus-Pro-745 (we name Janus-Pro-Edit). As shown in Table 2 Janus-Pro-R ---iit consis- tently outperformed Prous-rr--Editt on Pie-bench in both instruction following and unedited area preservation, which further validates the effectiveness of our approach.
Effect of Model Scale to Unlock Aha Moments. We further employ Janus-Pro- 1B as the backbone to explore whether smaller model could also achieve a breakthrough in visual generation going through the same training paradigm. As shown in Table Rows 8-10, in contrast to the significant performance improvement observed in Kanus-Pro-RI-1 image generation performance compared to the backbone. And the attempt to activate the Aha moments in this 1B model even compromises its initial image generation capabilities. This suggests that the CoT with deep thinking for visual generation requires a larger parameter size to be effectively handled, which is also an illustration of the scaling law.
Canus-Pro-RI-IB does not achieve superior
Effect of Data Quality in SFT. During SFT, when selecting positive images for subtask training, we establish a higher threshold larger than 0.5 of the semantic consistent score (S > 0.7 for comprehension tasks and S 2 0.8 for generation tasks). Though a higher threshold reduces the contrast, quantity of available training data, we find that it crucial for improving performance. we also set the threshold as S > 0.5 for selecting positive images in all three subtasks. As shown in Figure 5)),,, the decrease in data quality leads degraded T2I performance of both PSS and post-RL. RL. models. This highlights that the quality of SFT data is more important than its quantity.
Synergy of Sub-Tasks in SFT. We further demonstrate that in SFT, the text-to-image generation capability from Task-I and the regeneration abilities derived from Task- and Task-Ill are synergistic. We train two ablation models: 1roSS-P which undergoes only text-to- text-to-image -mage SFT with task-I, and Tanus-Pro-SFT Table Rows 6-7, in GenEval, the initial image generation quality of Janus- Pro-SFr superior to that of Janus-Pro-SFT--I and the final image generation quality after self-correction is superior to that of Pa-us-Pro-SFT-2. This indicates that the capabilities of simple text-to-image generation and multi-round image regeneration mutually enhance each other in a synergistic manner.
which undergoes the mixed SFT of task-ll and task-IlI. As shown in
SFT Memorizes, RL Generalizes. We compare the coxt-to-image performance change after the SFT phase (i.e., Janus-Pro-SFFT and the RL phase (i.e., Janus-Pro-R1) First, as shown in Table 4 Rows 1-5, After SFT, the performance improvement compared to the backbone model Janus-Pro-755 is minimal, with image regeneration not significantly improving the quality of the first generated image. This is because SFT tends to enable the model to imitate and memorize some sub-skills to develop the visual generation CoT, lacking the ability of generalization. In contrast, RL enhances
9

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Janus-Pro-SFT
Janus-PPe Janus-Pro-RI
phote of equare apple.
Prompti:
>
Nicel The image is squere which prompe
image is apple rether square anc. will
D
Nicel This is squored- Shape apple, which cligns with the prompt senothics
regenerate o new one.
shoto surple carrot yellow grope.
Prompt2:
image prompt, which is contoine the requined corret ond graps.
Wisti sarrct yalloa and the grape is parple with color ailecetion errer 3 regenenate
Nicel in Imoga, the gropes Though this doci not conmecs Jenos,
>
>
Prompt3:
Nicel There is image in the image, which consininnt witil caption.
Nical This on photo fish with lege, wnich consistent with poompt.
photo of fish with logs.
the fish within the image Iall regenenate new one.
Figure 6: Performance of Janus-Pro-SFT and Janus-Pro-RI for counterfactual generation.
generalization, significantly improving both the quality of the generated images and ensuring that the introspection process is genuinely effective. Of course, SFT is still essential, as it serves the role as cold-start and provides the foundation for the MLLM to explore reliable visual generation CoTs,
In Figure 6 we further present cases of counterfactual generation to highlight the differences between Janus-Pro-SFT and Janus-ProRRI. When given counterfactual prompts such as "a square apple", both models initially generate images that do not align with the prompt due to ingrained common-sense However, Lanus-ProSSFT deems the initial image reasonable as the final output. In contrast, Janus- Pro-RI identifies the semantic mismatches in the initial image and regenerates a new one that meets the requirements. This demonstrates that RI could generalize the original imitative behavior after SFT to genuine reasoning, thereby better avoiding the generation of hallucinations.
RL Paves the Way for Genuine Unified Visual Comprehension and Generation. The essence of unification for comprehension and generation should be reflected in the synergistic enhancement between the two capabilities. So what kind of training paradigm can promote such unification? From Tables (Rows 1-5) and we observe that although we can endow models with the foundational ability to collaborate visual comprehension and generation through SFT, the model appears to merely mechanically mimic and combine these two skills. The combination does not bring about any substantial improvement to either capability. Specifically, the performance of Janus-Pro-SFTI Pro-SFT-7B in both the visual comprehension task of image-text semantic consistency judgment and the text-to- image generation task shows very incremental improvement compared to Janus-Pro-77 through RL, we encourage the model to spontaneously integrate the two capabilities to explore reasoning pathways, and merely provide incentives as appropriate guidance. We find that the model learns how to better coordinate these two capabilities, resulting in stronger text-to-image generation and image semantic understanding abilities.
However,
Therefore, we argue that RL holds the potential to unlock genuine unified visual comprehension and generation. However, the current experiments are insufficient to conclusively validate this assertion. Owing to limited computational resources, our conclusion is based solely on simple image generation tasks and text-image semantic alignment evaluation tasks. Nevertheless, the experiments do provide some preliminary evidence for this conclusion. Given adequate computational resources, think that large-scal aarge-scale RL could feasibly be employed to achieve a synergistic enhancement of visual comprehension and generation for genuine unification, empowering MLLMs to demonstrate powerful image understanding and generation capabilities comparable to GPT-40.
5 Conclusion
In this paper, we enable MLLMs to form genuine CoT via deep thinking, advancing text-to- image generation into an iterative introspective process, thereby unlocking the Aha moments in visual generation. We introduce a two-stage training paradigm: SFT teaches the MLLM with the foundational ability to generate visual generation CoT with task decomposition, while RL effectively balances the exploration-exploittiie we further endow the MILM with unified visual generation capabilities, such as image editing. Extensive experiments demonstrate that our model achieves superior performance in both text-to- image generation and image editing tasks, also serving as excellent image semantic evaluator.
trade-off to unlock its full potential. On this basis,
10

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Acknowledgements
This work was supported by the Key R&D Projects in Zhejiang Province (No. 2024C01 106, 2025C01030), the NSFC (62272411), the Zhejiang NSF (LRG25F020001) and Ant Group, My Bank.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
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Appendix Overview
The anonymous project of our paper is in hattps://jamus-pro-rt gith.b. io In this supple- mentary material, we present:
Preliminary on GRPO in Section A
Implementation Details in Section
Evaluation Details in Section
More Experimental Results in Section D
Limitation, Future Work, Broader Impacts and Safeguards in Section
A
Preliminary on GRPO
Recently, reinforcement learning [16 27 has emerged as the primary method for unlocking the reasoning capabilities of LLMs. The study by [ introduces the Group Relative Policy Optimization (GRPO) framework. GRPO enhances Proximal Policy Optimization (PPO) 44] by eliminating the value function and estimating the advantages in a group-relative manner. Specifically, given the input instruction the old policy To.. first samples group of G individual responses as the response group 9 = {o ) We input each response with the group into the reward function to obtain the individual reward R,. We then calculate the advantages (A,) where each A; measures the relative quality of output compared the average reward:
R, mcan (R: )E
A
(4)
std (R. )
The GRPO method employs clipped objective function, similar PPO, and introduces KL divergence constraint that compares the current policy To with the reference model To..y into the loss function, as follows:
J(0)= E (g.0)~D
min
(p -, e.1 3Dx (Toll
(a:)--1 To (:l0)
>i
Oi
i=
1=1
(5)
where DKL (eert
1 is the the KL divergence maintain training stability. molor (1.00,))) is the ratio between the probabilities of my and Te for outputting the And Pt.j (o (,,
Trot log Fref
current token.
B Implementation Details
B.1 Supervised Fine-Tuning
For SFT, construct image-text collection C = (P, ((I), 1 1
the supervised
training data, where each prompt P corresponds to M images (I For each image-text pair, there is an associated semantic consistency score S E [0, 1] and a detailed reason R. for semantic matching (S 0.5) non-matching (S < 0.5), where S -t ... S.M Specifically, first develop a set of seed prompts and leverage the Qwen I model to expand them into total of N 200, 000 prompts, covering various types (color, shape. spatial, etc.) with 80, 000 short captions and 120, 000 long captions. For each prompt, we employ the FLUX (23] and Pro-sBPrr [S] models for text-to-image generation, producing M 18 images per prompt. Subsequently, we utilize the Intern the image and text are semantically consistent with the following prompt "Does this image match the description? Please directly respond with yes or no". We record the probability to answer "Yes" as Pves and "No" as Ps:, with prompt, we sort its associated M images based on their semantic consistency scores in descending order. In addition, to construct the long captions, we first collect some short captions. Subsequently, prompt Qwen to expand these short captions using the following prompt:
[6] model as the evaluation model, instructing it with the question of whether
Pree + Pso). And for each
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Please generate the long prompt version of the short according to the given examples. Long prompt version should consist of 3 to sentences. Long prompt version must sepcify the color, shape, texture spatial relation of the included objects. DO NOT generate sentences that describe any atmosphere.
Short: Case1-Short
Long: Casel-Long.
Short: Case1-Shov
Long: Casel-Long.
Short: Caption-Shot.
During supervised fine-tuning, we set = 3 for Task-Il and = 2 for Task-III, which means that allow the MILMS to generate up to three images (one text-to-image generation and two image regenerations) given a prompt. For mixed training, we set the mixing ratios for Task-I, Task-II, and Task-III as 0.2, 0.3, and 0.5, respectively. More training hyperparameters are detailed in Table5
Furthermore, given that Janus-Pro integrates two distinct types of image encoders, the understanding encoder is specifically the SIGLIP encoder [60]. while the generation encoder is the VQ tokenizer from [46]. For all input images in Task-II, the understanding encoder is employed to encode the input visual embeddings, whereas for all input images in Task-Ill, the generation encoder is used encode the input visual embeddings. Additionally, for Task-I and Task-Ill, each sample's 's input prompt P has 10% probability of being replaced with [PAD] tokens.
B.2
Reward Calculation
The overall design philosophy of our reward model is to leverage QA-based visual comprehension 6 36 22
25|
[ 30 29] models, which will return a consistency score RQ^(.) E [0, 1] for each text-image pair, to assess the accuracy of the generated image and the MLLM's self-evaluation. And we provide incentives for the final output images along with their preceding reasoning chains, incorporating bi-level reward scores: visual generation reward RGen and visual comprehension reward CComp, Assume that we permit the MLLM to perform up to a maximum of T-round image generations and the MLLM determines that has correctly generated the image in the K-th round (resulting in a total of K images J T 2 K), we have: (1) The generation reward i= -1 RQ4 (I)) + (T' K + 1) x (I)) The K-th image is assigned potentially larger weight because it represents the final output with higher importance. (2) The comprehension reward RComp
42
RGen
> (1 - (T+) - SE(T') ) x T,K, where SE(Z)= = 0 or is the self-assessment on whether the generated image is semantically aligned with the text.
To calculate the consistency score, we leverage Lntem2L2..-266 (6] as the reward model to provide appropriate incentives, which will return consistency score Ry E [0, for each text-image pair. Specifically, for short prompts, we directly the MLLM with the question "Does this image match the description? Please directly respond with yes no". We record the probability of the model responding with "Yes" as Pyes and "No" as Pro, with the consistency score calculated as RQA = Pyea/(C) (Pyes Pno).
For long prompts, inspired by 77 we first decompose the prompt into semantic tuples (e.g., attributes and spatial relations) and then generate corresponding yes-or-no questions (e.g., "Is the dog red?"). The reward model is then tasked with performing a VQA task for the prompt and the generated image, returning a score between 0 and I for each question in the same manner. The consistency score is obtained by averaging the evaluations of the reward model across multiple questions for given prompt.
B.3
Reinforcement Learning
During RL training, we use Janus-Pro-SFT as the backbone model and set the batch size to 128, the group size to 7, and 3 to 0.05. All parameters are tunable. We totally conduct 3000 iterations of post-training optimization. We find that the learning rate is crucial: a learning rate that too small results in insignificant performance gains, while a learning rate that is too large leads to unstable training. To address this, we design combined Linear + Cosine learning rate scheduler. The learning rate quickly drops linearly from peak value to lower "convert learning rate" at a "convert step".
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Table 5: The detailed training hyper-parametern of SFT and RL for both text-to-image and image editing tasks.
Hyper-paramelers
SFT for T21
RL for T21
SFT for Image Editing
RL for Image Editing
Optimizer
AdamW
AdamW
Adam
Adam
Optimizer param.
$1 0.9.92 0.95,
1e-6
B1 2.0e-5
0.9,
0.95,
1e-6
Peak LR
2.0c-5
6.0e-6
1.0e-5
Convert LR
2.0e-6
2.0c-6
Convert
400
300
Min LR
2.0c-7
2.0c-7
2.0c-7
2.0c-7
scheduler
Cosine
Linear+Cosine
Cosine 128
Cnnear
Batch size
128
128
128
Group size
7
8
0.05
0.05
Truining Steps
50K
3K
40K
2.2K
Warmup Steps
1000
100
1000
100
Weight decay
0.05
0.05
0.05
0.05
Gradient clipping
1.0
1.0
1.0
1.0
Numerical precision
blooat16
b6loat16
b6loat16
biloat16
Resource Usage
NVIDIA A800
NVIDIA A800
NVIDIA A800
32 NVIDIA A800
and then gradually decreases along cosine curve. However, still encounter some instability during training, indicated by downward trend in the reward curve. To address this, we adopt the following measures:
(1) When the reward curve dropped sharply, we reduce the learning rate to half or two-thirds of its current value and resume the training:
(2) When the reward curve declines gradually, it indicates that the KL divergence constraint imposed by a less capable reference model is limiting further improvement of the current model. To address this, we propose two measures: If the optimization steps applied the current model relative the reference model are small, we reduce the KI. divergence constraint weight by setting a smaller B value and then resume training. Otherwise, we update the reference model to the current model and then resume the training.
The detailed hyperparameters for training are shown in Table
B.4 Inference
Janus-Pro- can generate both text and images with two separate MLLM heads: an image head for predicting image tokens and a text head for predicting text tokens. We transform the text-to-image During inference, when provided with prompt, the model initially engages the image head to perform an autoregressive prediction of 576 visual tokens to generate the first image. Subsequently, it switches to the text head to assess whether the generated image aligns with the semantic of the prompt and to predict a relevant reason. If the model determines that the image is consistent with the prompt, the inference is terminated, and the generated image is output. Conversely, if the model deems the image inconsistent, it switches back to the image head to predict another 576 visual tokens for new image generation attempt. The model then reverts to the text head to reassess the semantic alignment and predict a reason. This cycle of switching between the image and text heads continues iteratively. Of course, to prevent infinite image generation, the model is constrained to generate a maximum of 3 images for each inference.
generation into an introspective process [1
37 38
Furthermore, considering that Janus-Pro incorporates two types of image encoders, during image generation with CoT, we select the image encoder in the following manner: when the model leverages the text head to output text (Task-II), all preceding images encoded using the understanding encoder. Conversely, when the model leverages the image head to output image tokens (Task-Ill) all preceding images are encoded using the generation encoder.
Moreover, we set topk 50 for text token sampling and topk 4096 for visual token sampling. Besides, for text-to-image generation (Task-I) and image regeneration (Task-III), during inference we use classifier-free guidance on the logits for autoregressive sampling in a manner similar to We set the guidance scale to 5.0. It is important to note that during both text-to-image generation and image regeneration, we only mask the input text prompt P with [PAD] tokens to facilitate classifier-free ffre guidance.
39 52 28
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for B.5 Implementation Details of Image Editing
Supervised Fine- Tuning. We first collect image editing training data from 662 and [59]. Given the suboptimal quality of existing image editing datasets, we first conduct a data cleaning process on the training data. With Intern VL2.55226B [6 the evaluation MLLM, we provide it with before-and- after-editing images along with editing instructions, and then pose the following two questions for each image editing training sample (I", ,pins, I inspired by [15):
(I) Instruction Following Question: Does the edited image follow the instruction? Please directly respond with yes or
(2) Detail Preserving Question: Are the non-edited areas of the edited image consistent with the original image? Please directly respond with yes or no.
For each question, we recorded the probability of the evaluation MLLM responding with "Yes" as pff Paas yes yes and Pffl" or Pres, with following score calculated S (I', Iout) pfl (Pafla yes yes + Pflw) and preserving score as Spee (I", "iins, IIou) ppae / PPe + (ho))) For no each sample, we incorporate it into the training dataset only if both S piins I 2 0.7 pins and 5 (Ie, Ioul) 2 0.7. Furthermore, during SFT, each sample's instruction has a 10% probability of being dropped out as [PAD] tokens. And we detail the training image editing SFT in Table
ges
Reinforcement Learning. During reinforcement learning, we no longer provide ground-truth
edited images. Instead, we allow the model to autonomously predict the edited images based on the input images and instructions, only providing proper incentives according to the GRPO algorithm.
We still leverage Intern V1.2.5-26B (6] as the reward model and design two QA-based rewards: (1)
Following score //w E [0, 1] measures whether the model accurately follows the editing request; (2)
Preserving score R E [0. i] indicates how well the model preserves details that are not intended to be changed. The calculation methods for these two scores are identical to those described in the preceding paragraph for computing the following score and the preserving score. And the final reward is calculated as R Redit 0.5 R/w + R where Riw is assigned with relatively low weight prioritize enhancing the model's ability to maintain local fidelity. And we find that a smaller weight for Ryt tends to lead the model to output the pre-edited image directly in order achieve higher overall reward value. Furthermore, we set the group size to 8 and the batch size to 128. The trick that enables stable RL training similar to that described in Appendix B.3 We provide additional details of the hyperparameters for image editing RL in Table
Inference. During inference, the input images are encoded using the generation encoder. We set topk = 4096 and also employ classifier- free guidance on the logits for autoregressive sampling with the guidance scale of 4.0. We only mask the input text instruction pine with [PAD] tokens to facilitate classifier-free guidance without masking the input image.
C Evaluation Details
C.1 Baseline Methods
For text-to-image generation tasks, we compare Janus-Pro-Ri with diffusion-based and MLLM- based methods, as well as MLLMs incorporating CoT into image generation. The diffusion-baai
basclines include PixArt-alpha A, DALL-E3 (2) SD3 D. FLUX.I-UUO I-dev [23], Sana-1.5 [57]. and
Janus-Flow BII The MLLM-based baselines include Show-o [58) SEED-X [12, Emu-3 [52)
DDT-LLaMA (39, VARGPTv1.1 (44, Infinity [8], Janus- Pro (5), GPT-4o 34 The MLLM+CoT
baselines include Show-o+PARM [17, MINT (3), GOT [10]. T21-R1
21 To ensure fair
comparison, for the last category of baselines, we report results without inference-time scaling.
For image editing tasks, we compare Janus-Pro-RI with instruction- based large-scale training meth- ods, including InstructPix2Pix (3), MagicBrush InstructDiffusion MGIE [T. Seed-X- Edit [12.. and EditAR (32.
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C.2 Evaluation Benchmarks
conduct zero-shot evaluation on existing text-to-image benchmarks: GenEval 14]. T2I- CompBench [20], and DPG-Bench [19]. GenEval contains 6 different subtasks of varying diffi- culty requiring various compositional skills, including single object (SingObj), single object (TwoObj), counting. colors, position, color binding (ColorAttri). And adopt the metric proposed by [14] for evaluation. Each subtask is scored independently, and the overall score is calculated the average of all six subtask scores. The T21-CompBench encompasses three subtasks following [2]: color, shape, texture. Building on prior research, we employ the Blip-Von VQA score [26 the evaluation metric. For DPG-Bench, follow the metries proposed in 19 to conduct the evaluation.
For the image editing task, we conduct zero-shot evaluation on Pie-bench [22] with 700 examples, covering 10 editing types. Our method uses the source image and editing instructions to predict the target edit. Both reconstruction and text-to-image alignment are evaluated as in [22] with the annotated foreground masks, with structure distance and background preservation metrics reflecting fidelity preservation, while CLIP similarity is used for editability evaluation.
Nice! The image is consistent with the coption, showing yellow bird and black notorcycle.
image ablong image. regenerate
Nical New both globe and obleng frane. which meets the user's requirementss
>
coption.
photo yellow
motorcycle
sppeerical globe oblong
Watt! think the two pieces of cheese in the image do not match the three pieces of cheese mentioned the coption, will regenerate nee one.
Nice! The consistent with the caption, showing two cons of soda and the hamburgers.
Nicel New there and three of cheese the which motches the oxption
a photo of two cans of soda and two hamburgers
a photo of two rats and three pieces of cheese
Watti notice that there mismtch between the image and the caption. ned sunflower is mixed with the blue surfloweent will regenerate may one.
Nixel Now all the sunflowers in the image are blus, and thene also red moon in the image. Therefore, believe this image meets the user requinments.
consistent the and white
photo of red cat and white teddy bear
photo of blue sunflowers and red moon
Weit! think the posttions the mage de not match the coption. The Natormelon. should be the leftmest frui in the image I regerenste new one.
Nicel New the pincopple to the left of the apple. and the watermelon is the of the pinnopple. which motches cop
consistent
>
showing flying balloon.
bird
photo of flying bird to the left of the balloon
photo of pineapple to the left of apple and watermelon to the left of the pincapple
Wait! do notl des tie in the imge and image is inconsistent with prompt: I regenerate new
Nice consisiton with the prampt see photo of right motoroycle.
>
>
instoryycle, regenenots
photo of tie right of matorcycle
Woltf The does match the There braccali the image I negenerate new one.
parking
Nicel The consistent with the there parking meter abowe broccoli.
>
merged togethen: will
photo of parking meter above broccoli
think
Waiti think the image is inconsistion with the coption becouse the stop sign is not white, I will reginerate near one.
Nicel I think good image with e brown giroffe white shop sign.
>
nother blury.
STOP
>
STOP
>
photo brown giroffe and white stop sign
Figure More qualitative examples of introspective text-to-image generation that triggers Aha moments.
D More Experimental Results
we present more qualitative examples of Janus-Pro-RI to trigger Aha moments within its reasoning chains to generate superior images. Our model could leverage its visual comprehension capabilities accurately identify the issues its initial-generated images, then unleash the visual generation capabilities output more accurate image.
Figure 7
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photo of watermelon three oranges
photo of square and a round tin
photo round bag apple yellow pear
of
photo of green nose blue tulip
photo loptop above sports ball
photo cubic block and cylindrical bottle
cylinder and blue cube
Ours Janus -er--roo-- 7B
square box
picture of three banonos and apples
photo a blue cylinder and red
photo blue bird and bear
photo blue opple and green
a photo two pineapples and three
painting metallic ring and wooden knife
e photo weoden spoon and metollic
a photo white cloud and black sky
Ours Janus-Pro-78
photo of brown and blue cup
photo of a brown bird and
photo of a yellow apple pear
picture cylindrical block and
picture of blue cow cup
a picture of a black cloud and sky
photo of blue rose and green tulip
picture of threee books
Ours Janus Janus-Pro-72 Pro-7B
submanine glides silently bereath the directly passing ship
bright bulb provides ambient light inside the ornate glass lontern
fleoning adds wormth to the room, consisting wall carpet
picture of apple, two mangoes, peaches
picture of a gloss metallic cup
of small pig ond a big
landscape stretches out cool, night skey
picture green apple and blue vase
Ours Janus Pro 7B
depicting historical
topestry
small
clung precoricusly its delicate web
por portfolio contains sketches of preciscly nine different architectural designs
poir of skis stonds
gloss stands eleguntly on the coaster. positioned to the left of the cabin
positioned right the cheese platter
The water in the swimming pool slightly marky green
single rase lies elegontly beside the crystal vose, ploced centrally on the dining table
drawing depicts
exactly chimneys
house with
child's
battle hung
Ours Janus Pro-7B
castle woll
puffing snoke
Figure 8: Qualitative Comparisons between Manus-Pro-77 and our Mauus-ProRR1-75 on the final generated image for text-to-image generation task.
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Figure 8 presents direct qualitative comparison between Janus-Pro-7B and our Manus-Pro-Rul on the text-to-image generation task, with both short and long captions. It can be observed that compared to Janus-Pro-7Th after unlocking the Aha moments with CoT via a two-stage training paradigm, our model not only generates images that are more semantically aligned with the text but also achieves higher aesthetic quality.
E
Limitation, Future Work, Broader Impacts and Safeguards
Limitations. Firstly, in the text-to-image generation task, our constructed prompt data contains relatively few instances related counting. This directly results in our model's counting metric the Geneval dataset lagging behind SOTA models. To address this issue, we will further specifically construct set of counting-related data enhance the corresponding capabilities for counting-related prompts. Secondly, in the image editing task, although the existing datasets meet the requirements for image editing, many of the data samples have poor aesthetic appeal. This leads some edited images lacking in aesthetic quality. To improve this, we plan to use GPT-4o to create a set of high-aesthetic image editing data in the future to enhance the natural appearance of the edited images.
Future Work. In the future, we plan to harness more substantial computational resources to design increasingly complex interleaved text-image generation tasks. We are convinced that reinforcement learning has the potential to bring about revolutionary changes to unified visual comprehension and generation, thereby significantly raising the upper limit of such unification. To this end, we will cover a broader range of visual understanding and visual generation tasks, seamlessly integrate these two capabilities, and tackle the open-ended interleaved text-image generation problem. Ultimately, hope to transform MILMs into more intelligent multimodal dialogue system.
Broader Impacts. This study does not raise any ethical concerns. The research does not involve subjective assessments or the use of private data. Only publicly available datasets are utilized for experimentation.
Safeguards. A major societal concern with this technology lies in its potential for misuse, par- ticularly in fabricating unauthorized images that could lead misinformation, privacy breaches, and other damaging consequences. To counter these threats, it is crucial to develop strong ethical standards and implement ongoing surveillance.
29


Reviews:
[{"review_id": "D0e7DFeDIO", "reviewer": "Reviewer_yXuE", "summary": "The paper proposes a post-training pipeline for unified MLLMs that turns text-to-image generation into a multi-turn image editing via CoTs , and shows Janus-Pro-R1, which is trained from a base model Janus-Pro. The paper shows that after a two-stage training pipeline (mixed SFT on three subtasks followed by GRPO-based RL), the model shows self-correction patterns (similar to the \"Aha moment\" in o1-like models) and is able to handle text-to-image and image editing better.", "questions": "1. Image editing may cause inconsistency, especially with many turns, where the mismatch and error will accumulate. Would this affect the final results, e.g., make longer CoT worse than short CoTs?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "- Strengths\n\n1. The motivation and idea is interesting. Turning text-to-image generation into a multi-round image editing bridged by (text-space) reasoning CoTs is reasonable.\n2. The evaluation on public text-to-image generation benchmarks shows good empirical results. Janus-Pro-R1 tops or matches SOTA across three compositional T2I benchmarks and markedly improves PIE-Bench editing fidelity/editability trade-offs.\n3. The authors provide good ablations on task synergy, reward models, and model size.\n\n- Weaknesses\n\n1. The RL optimises scores provided by InternVL2.5, which may suffer from reward hacking and can be inconsistent.\n2. The prompt set is purely synthesized with Qwen, and the SFT images are generated by pre-trained diffusion models such as FLUX. This can cause worse diversity.\n3. Turning text-to-image generation into multi-round editing task can introduce much higher computational overhead. The authors should discuss about the computational complexity in their paper.\n4. Lack of user study on realism/editability, so practical impact is inferred solely from automated metrics.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your valuable comments. We are encouraged that our motivation and idea are recognized as interesting. We will explain your concerns as follows.\n\n> **(I) Weakness1:** The RL optimises scores provided by InternVL2.5, which may suffer from reward hacking and can be inconsistent.\n\n**A1:** Thank you for your question. Of course, we want the chosen RL reward score to mitigate reward hacking as much as possible and remain as aligned as possible with genuine human evaluation preferences. In selecting the reward model, we considered a wide range of candidates. After empirically comparing various vision-language models (VLMs), we found that InternVL2.5-26B is the best open-source VLM for most reliably assessing image–text semantic consistency while achieving the closest alignment with human evaluation.\n\nTo illustrate the above conclusion, after collecting hundreds of images generated by Janus-Pro, we score them using human evaluation, and vision-language model (VLM) evaluation, including GPT-4o, InternVL2.5-8B/26B, LLaVA-1.6-34B, ShareGPT4V-13B, and Qwen2.5-VL-32B. We compare the score differences between the VLM evaluation results and the human evaluation results. As shown in the following table, **InternVL2.5-26B has a strong ability to judge fine-grained image-text consistency among open-source models** (even nearing the performance of GPT-4o), with its **evaluation results very close to those of human evaluation**. Thus, we chose InternVL2.5-26B as the evaluation model, effectively mitigating the issue of reward hacking.\n\n**Table:** Score difference between VLM evaluation and human evaluation.\n| VLM | Score Difference$\\downarrow$ |\n|-|-|\n|GPT-4o| **0.06** |\n|InternVL2.5-26B| ***0.09*** |\n|InternVL2.5-8B | 0.20 |\n|LLaVA-1.6-34B | 0.17 |\n|ShareGPT4V-13B | 0.19 |\n|Qwen2.5-VL-32B | 0.11 |\n\nOf course, we are also considering using GPT-4o as the reward model, or combining different VLMs for different prompt configurations to provide reward scores, thereby further reducing reward hacking and improving evaluation consistency. However, this would inevitably increase training costs, so we defer this to future work.\n\n&nbsp;\n\n> **(II) Weakness2:**: The prompt set is purely synthesized with Qwen, and the SFT images are generated by pre-trained diffusion models such as FLUX. This can cause worse diversity.\n\n**A2:** **Firstly**, to maximize prompt diversity, we begin by hand-crafting hundreds of seed prompts and a modular set of instruction templates for prompt extension. The seed prompts with instruction templates span dozens of semantic axes (including counting, tone, spatial relationships, color, textual content, and many more), while also embracing a broad spectrum of descriptive styles. With Qwen as the tool LLM, we iteratively expand the seed prompts into a much larger and more heterogeneous prompt pool.\n\nFurthermore, to prevent the generated prompts from being uniformly short captions, we also instruct the LLM to upscale the short prompt into long and detailed descriptions. As a result, we construct ~200k prompts **spanning diverse content categories and descriptive styles, ranging from ultra-brief captions (<10 words) to richly detailed narratives (>150 words)**.\n\n&nbsp;\n\n**Secondly**, to generate the SFT images, we leverage both FLUX and Janus-Pro. Each model brings distinct strengths: FLUX excels in overall aesthetic quality and performs more reliably on counting-related prompts, whereas Janus-Pro achieves superior semantic alignment for prompts involving spatial position or color. Even within a single model, the multiple images generated for the same prompt can yield markedly different visual styles and semantic alignment.\n\nTherefore, for nearly every prompt, our final collection of $M=18$ images **not only includes high-quality SFT samples but also exhibits deliberate stylistic and quality divergence**, which simultaneously satisfies the diversity requirement and aligns with our goal of curating a quality-graded dataset.\n\n&nbsp;\n\n> **(III) Weakness3:**: The authors should discuss the computational complexity in their paper.\n\n**A3:** For computational overhead **during training**, we perform post-training (SFT+RL) on the pre-trained Janus-Pro model. The entire post-training lasted ten days: four days of SFT on 8 A100 GPUs and six days of RL on 32 A100 GPUs. **We consider this overhead well-justified**, as it equips Janus-Pro with the emergent ability to generate interleaved image–text sequences and, more importantly, unlocks its aha moment for visual generation.\n\nRegarding **inference**, we first acknowledge that activating the aha moment for image generation incurs higher time overhead: on average, *each round of image regeneration adds roughly 1.25× the cost of a standard text-to-image pass*. However, it is important to note that the model triggers a second round of image generation only if it self-evaluates that the initially generated image is semantically misaligned. For the vast majority of prompts, the model produces a faithful image in the first round, eliminating any need for regeneration. In these cases, the model only infers a handful of additional text tokens for self-evaluation, **incurring virtually no extra computational overhead compared to conventional text-to-image generation.**\n\nImage regeneration is invoked only when a semantic error is detected during self-evaluation. Although invoking image regeneration increases computational overhead, **it enables self-correction and yields output images that are more semantically aligned with the prompts** (see Table 1: \"with Aha\" vs. \"w/o aha\"). Echoing the design philosophy of models such as DeepSeek-R1 and GPT-4-o1, we view trading extra inference time for higher generation accuracy as a worthwhile investment.\n\nFurthermore, **the trade-off between image quality and computational overhead is also fully controllable** by the users, who can flexibly define a maximum number of regeneration rounds as needed. For instance, we limited the model to at most two regenerations (a maximum of 3-round image generations) to deliver near-optimal visual quality while keeping time overhead within an acceptable range.\n\n&nbsp;\n\n> **(IV) Weakness4**: Lack of user study on realism/editability, so practical impact is inferred solely from automated metrics.\n\n**A4:** We sincerely apologize that practical impact is inferred solely from automated metrics. In our paper, besides automated evaluation, we provide qualitative comparisons of text-to-image generation in Appendix Fig.1 and image editing in MainPDF Fig.4. To further provide a solid user study on realism/editability, we construct a comprehensive test set, including 100 image editing test cases and 100 text-to-image generation test cases, covering a wide range of editing instructions and prompt categories.\n\nWe conduct a rigorous human evaluation, where human evaluators rate each generated image on an overall 1-to-5 scale, jointly weighing diverse dimensions including realism and editability. As shown in the following tables, **for human evaluation, our model exhibits superior performance to open-source baseline methods in both tasks** (only marginally underperforms GPT-4o), which demonstrates the strong performance of our model.\n\n**Table:** Human evaluation on text-to-image generation\n| Model | Human Score$\\uparrow$ |\n|-|-|\n|FLUX.1-dev|3.7|\n|Sana-1.5|3.3|\n|Show-o|2.6|\n|Janus-Pro|3.0|\n|T2I-R1|3.4|\n|GPT-4o|**4.2**|\n|**Ours**|***4.0***|\n\n**Table:** Human evaluation on image editing\n| Model | Human Score$\\uparrow$ |\n|-|-|\n|InstructPix2Pix|2.7|\n|MagicBrush|2.5|\n|Ultraedit|3.1|\n|SeedX-Edit|2.8|\n|EditAR|3.0|\n|GPT-4o|**4.0**|\n|**Ours**|***3.5***|\n\n&nbsp;\n\n> **(V) Question1**: Image editing may cause inconsistency, especially with many turns, where the mismatch and error will accumulate. Would this affect the final results, e.g., make longer CoT worse than short CoTs?\n\n**A5:** Thank you for the question; we would like to clarify potential misunderstandings from two perspectives.\n\n**(1)** For text-to-image, the generated image only needs to align with the semantics of the user prompt. After reformulating text-to-image generation as a multi-round image-editing process, we regenerate the image in each round solely to rectify any semantic misalignment between the current image and the prompt. **Inconsistencies in prompt-unrelated regions across the images from different rounds do not create mismatches with the prompt; consequently, accumulated inconsistency does not degrade the performance of text-to-image generation.**\n\nThe table below reports the results on Geneval and T2I-CompBench when the aha moment is triggered from 0 to 2 times. It can be seen that **the performance of longer CoTs (triggering more Aha moments) still surpasses that of shorter CoTs.**\n\n**Table:** Performance on text-to-image generation with longer/shorter CoTs.\n\n| CoT Length | GenEval (Overall) | T2ICompbench (Avg) | DPG-Bench |\n|-|-|-|-|\n|w/o Aha|0.83|70.3|85.02|\n|with Aha (1time at most)|0.85|72.0|85.41|\n|with Aha (2times at most)|**0.86**|**72.7**|**85.57**|\n\n&nbsp;\n\n**(2)** After upgrading the t2i model to multi-turn image editing, we observe that the model also becomes better aligned with the image-editing task itself. While image editing additionally necessitates preserving image fidelity, when leveraging Janus-Pro-R1 for this task, we do NOT introduce Aha moments with image regeneration for each turn of editing to avoid accumulating inconsistencies. As shown in Fig.4, compared with baseline methods, for each turn of editing, the output images of our model consistently resemble the source images while remaining coherent with the instructions.\n\n&nbsp;\n\nThank you once again for your constructive suggestion! We are grateful for the opportunity to improve our work based on your insightful comments."}, {"review_id": "foXKSd6tmZ", "reviewer": "Reviewer_xYFR", "summary": "Recent MLLM research aims to unify visual comprehension and generation, but these capabilities remain disjoint, failing to mutually reinforce. This paper proposes collaborative co-evolution of the two, transforming image generation into an iterative introspective process via a two-stage training: supervised fine-tuning for Chain of Thought (CoT) generation and reinforcement learning for exploration-exploitation balance. The approach advances MLLMs from text-to-image tasks to unified image generation. Experimental results demonstrate the model's superior performance in both text-to-image synthesis and image editing scenarios.", "questions": "1. The paper uses Qwen to generate 20,000 prompts for constructing the training set. May I ask how the authors ensured the diversity of these prompts?\n2. The paper aims to enhance the reasoning ability of Janus-Pro by constructing CoT data. May I ask why it is necessary to use both Flux and Janus-Pro to generate images simultaneously, and what is the significance of image generation by Flux?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n1. It uses a two-stage training paradigm to collaboratively enhance the visual comprehension and generation within MLLMs. By constructing genuine CoT, it unlocks the \"aha moments\" for T2I generation, enabling a more intelligent and coherent generation process.\n2. The method extends the traditional T2I generation to image editing scenarios, thus unleashing the potential for unified image generation. This expansion broadens the practical applications of MLLMs in the visual domain.\n3. The model based on this method achieves a new SOTA performance in both T2I generation and image editing, demonstrating its effectiveness and superiority over existing approaches.\n\n**Weaknesses**\n1. From the results in Table 1, the performance improvements brought by \"w/ Aha\" are relatively marginal, which reduces the solidness of the method proposed in this paper.\n2. In Table 2, the method proposed in this paper shows poor performance in the Image Editing task, even falling behind many methods based on Stable Diffusion v1.5.\n3. Since the GRPO algorithm has gained significant popularity, it may not be necessary for the authors to elaborate extensively on related content in the \"Method\" section.", "was_revised": true, "final_justification": "The rebuttal has addressed my concerns very well, so I am willing to increase my rating to 4.", "rebuttal": "Thank you for your comprehensive comments. We are encouraged that our work is recognized to broaden the practical applications of MLLMs in the visual domain. We will explain your concerns as follows.\n\n> **(I) Weakness1:** The performance improvements brought by \"w/ Aha\" are relatively marginal.\n\n**A1:** Thank you for your question. We aim to clarify your concerns from two perspectives.\n\n**Firstly,** we want to claim that **activating the \"Aha moment\" yields clear capability gains**, especially in the widely-used benchmark GenEval. Specifically, as shown in Table 1, in GenEval, our model gains a 3-point boost in overall performance only considering the first-round text-to-image generation (\"w/o Aha\") compared to the backbone model Janus-Pro (from 0.80 to 0.83). When further unlocking Aha moments to introduce image regeneration (\"with Aha\"), the overall performance ascends an additional 3 points (from 0.83 to 0.86). Such improvement is not incremental on this benchmark. \n\n\n**Secondly**, although the performance gain introduced by \"with Aha\" over \"w/o Aha\"  is smaller than that of “w/o Aha” over the backbone model, it is expected and actually evidences the efficacy of our first-round image-generation capability. Specifically, during reinforcement learning, we encourage the model to satisfy the prompt **in as few rounds of image generation as possible**: **the fewer rounds it needs to produce a correct image, the larger the reward we provide.** For instance, if the model can generate a compliant image and perform an accurate self-evaluation in the first round, it could receive the maximum reward score.\n\nConsequently, compared with the backbone model, we have achieved a marked improvement in first-round-generated image quality. In the vast majority of cases, the model directly generates images that satisfy the requirements without any need for regeneration. Only when the initial generation is incorrect does the model invoke the Aha moment as the self-correction.  **Because such cases constitute only a small minority, the activation of the Aha moment acts as a refinement rather than a primary driver of performance.** Therefore, it is both reasonable and expected that the gain of \"with Aha\" over \"w/o Aha\" is smaller than that of \"w/o Aha\" over the backbone model.\n\n&nbsp;\n\n> **(II) Weakness2:** In Table 2, the method proposed in this paper shows poor performance in the Image Editing task, even falling behind many methods based on Stable Diffusion v1.5.\n\n**A2:** We apologize for not providing sufficient context about the benchmark, which may lead to some misunderstanding of the metrics.  In Table 2, PIE-Bench evaluates how well an image-editing model balances the trade-off between fidelity preservation and instruction following. Specifically, the first five metrics (Structure Distance and Background Preservation) in Table 2 evaluate fidelity preservation, i.e., how faithfully the model retains all details that should remain unchanged. While the final two metrics (CLIP Similarity) evaluate instruction following, that is, how accurately the model executes the requested edits.\n\n**Judging which model is superior requires considering both fidelity preservation and instruction following simultaneously**: all six metrics must be compared as an integrated whole, and focusing on a single dimension yields no meaningful insight. For example, though some baselines based on SD v1.5, such as MagicBrush, outperform our model on the first four metrics, they achieve this through an unacceptable shortcut: **in many editing cases, they simply return the unaltered source image as the post-edit output. This \"zero-edit\" trick grants them perfect fidelity scores,** yet it is clearly the opposite of what an editing model should do. Consequently, the last two metrics expose their poor editability and **render the high scores on the first four metrics meaningless.**\n\nTherefore, when evaluating a method's performance, we should assess whether it strikes a good balance between fidelity preservation and instruction following, rather than whether it achieves the highest scores on any single metric. Among baselines, **many methods based on SD v1.5, such as MagicBrush, obtain good fidelity preservation but the worst editability.** Only EditAR shows a relatively good trade-off between these two dimensions. In contrast, our **Janus-Pro-R1-Edit achieves better overall performance** and maintains a good balance between fidelity and editability, delivering the most effective performance.\n\nMoreover, as the community still lacks a standardized, objectively reliable automatic metric for image editing,  human judgment remains the de facto gold standard. To further showcase the superiority of our approach, we curated 100 diverse test cases, ranging from simple to highly complex edits. We conduct a rigorous **human evaluation**, where human evaluators rate each generated image on an overall 1-to-5 scale, jointly weighing diverse dimensions including fidelity preservation and instruction following.\n\nAs shown in the following tables, **our model exhibits superior performance to open-source baseline methods for human evaluation of image editing** (only marginally underperforms GPT-4o), which demonstrates the strong performance of our model.\n\n**Table:** Human evaluation on image editing\n| Model | Human Score$\\uparrow$ |\n|-|-|\n|InstructPix2Pix|2.7|\n|MagicBrush|2.5|\n|Ultraedit|3.1|\n|SeedX-Edit|2.8|\n|EditAR|3.0|\n|GPT-4o|**4.0**|\n|**Ours**|***3.5***|\n\n&nbsp;\n\n> **(III) Weakness3:** It may not be necessary for the authors to elaborate extensively on related content in the \"Method\" section.\n\n**A3:** Thank you for your suggestion. In fact, we have extended the application scope of GRPO, and the Method section primarily details how this algorithm is adapted to our specific task setting.\n\nConventional GRPO is employed for single-modal token generation (generating text or visual tokens). In this paper, we extend it to interleaved text-image generation with dual-level rewards: comprehension rewards provide appropriate incentives for textual token generation, while generation rewards provide incentives for visual token generation. Besides, different samples within a group may entail varying numbers of image-generation and self-evaluation rounds. Consequently, applying GRPO in this setting introduces new challenges, such as how to integrate the two rewards and how to assign fair scores to samples with differing numbers of image-generation rounds.\n\nThus, in the Method section, we provide a step-by-step elaboration of GRPO, **highlighting the precise modifications introduced to accommodate dual-level rewards and interleaved text–image generation**, enabling readers to understand our approach fully. In the revised manuscript, we will condense this section as you suggest.\n\n&nbsp;\n\n> **(IV) Question1:** How do the authors ensure the diversity of these prompts?\n\n**A4:** We totally constructed 20,0000 prompts for SFT. To maximize prompt diversity, we begin by hand-crafting hundreds of seed prompts and a modular set of instruction templates for prompt extension. The seed prompts with instruction templates span dozens of semantic axes (including counting, tone, spatial relationships, color, textual content, and many more), while also embracing a broad spectrum of descriptive styles. With Qwen as the tool LLM, we iteratively expand the seed prompts into a much larger and more heterogeneous prompt pool.\n\nFurthermore, to prevent the generated prompts from being uniformly short captions, we also instruct the LLM to upscale the short prompt into long and detailed descriptions. As a result, we construct ~200k prompts **spanning diverse content categories and descriptive styles, ranging from ultra-brief captions (<10 words) to richly detailed narratives (>150 words)**.\n\n&nbsp;\n\n> **(V) Question2:** Why is it necessary to use both Flux and Janus-Pro to generate images simultaneously, and what is the significance of image generation by Flux?\n\n**A5:** Janus-Pro and FLUX excel in different dimensions of text-to-image generation: FLUX delivers superior overall aesthetic quality and is more reliable on counting-related prompts, whereas Janus-Pro achieves stronger semantic alignment for prompts that specify spatial position or color. Meanwhile, the two models also produce different visual styles for the same prompt.\n\nBy invoking both FLUX and Janus-Pro concurrently, **we leverage the complementary strengths of each model**. Across nearly every prompt, the final set of $M=18$ images contains **not only high-quality SFT samples but also deliberate variations in style and quality**. This simultaneously satisfies the diversity requirement and advances our goal of curating a quality-graded dataset.\n\n&nbsp;\n\nWe hope we have addressed all of your concerns. Discussions are always open. Thank you!"}, {"review_id": "QIyjIQp6dS", "reviewer": "Reviewer_Fzpc", "summary": "The main paper proposes a training framework for text-to-image generation tasks with multimodal large language models (MLLMs) by jointly addressing the visual comprehension and generation. \nThe proposed method combines supervised fine-tuning with reinforcement learning based on Group Relative Policy Optimization (GRPO), which appears to improve performance on text-to-image generation tasks.", "questions": "1. Does the proposed method compromise performance on visual understanding tasks?  \nAs discussed in *MetaMorph [A]*, visual understanding and generation are considered synergistic. Could the proposed approach negatively affect understanding performance?\n\n\n2. Given that Janus-Pro does not have a particularly large context window, what is the typical value of T used during training?  \nAdditionally, how does the distribution of $K_i$ evolve as training progresses?\n\n\n**Reference**  \n[A] S. Tong et al., MetaMorph: Multimodal Understanding and Generation via Instruction Tuning, ICCV 2025.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strength** \n1. The writing is clear. \n\n2. The method is simple and technically sound.\n\n\n**Weakness**\n1. The analysis regarding the judge model appears to be limited. I am curious about the effect of the judge model on the overall performance. Have the authors considered evaluating alternative judge models besides InternVL-26B?\n\n2. The qualitative results focus mostly on relatively simple image editing cases. It is unclear how well the model performs on more challenging tasks, such as multi-object editing.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank you for your insightful feedback. We are encouraged that our method is recognized as technically sound. We will explain your concerns point by point.\n\n> **(I) Weakness1:** Why choosing InternVL-26B as the judge model?\n\n**A1:** Thank you for your question. \nOf course, the choice of judge model is crucial to overall performance; we therefore require the judge model to deliver an impartial and objective assessment of image quality. After empirically comparing various vision-language models (VLMs), we found that InternVL2.5-26B is the best open-source VLM for most reliably assessing image–text semantic consistency while achieving the closest alignment with human evaluation.\n\nTo illustrate the above conclusion, after collecting hundreds of images generated by Janus-Pro, we score them using human evaluation, and vision-language model (VLM) evaluation, including GPT-4o, InternVL2.5-8B/26B, LLaVA-1.6-34B, ShareGPT4V-13B, and Qwen2.5-VL-32B. We compare the score differences between the VLM evaluation results and the human evaluation results. As shown in the following table, **InternVL2.5-26B has a strong ability to judge fine-grained image-text consistency among open-source models (even nearing the performance of GPT-4o), with its evaluation results very close to those of human evaluation.** Thus, we chose InternVL2.5-26B as the judge model, which can provide an impartial and objective assessment of image–semantic alignment to serve as incentives.\n\n**Table:** Score Difference Between VLM Evaluation and Human Evaluation\n| VLM | Score Difference$\\downarrow$ |\n|-|-|\n|GPT-4o| **0.06** |\n|InternVL2.5-26B| ***0.09*** |\n|InternVL2.5-8B | 0.20 |\n|LLaVA-1.6-34B | 0.17 |\n|ShareGPT4V-13B | 0.19 |\n|Qwen2.5-VL-32B | 0.11 |\n\n&nbsp;\n\nWe have also experimented with using Qwen2.5-VL-32B as the judge model for RL training, keeping all training settings consistent with the main experiments in our paper. As shown in the following table, switching the judge model from InternVL2.5-26B to Qwen2.5-VL-32B to provide rewards results in a decline in the model’s text-to-image performance. This further demonstrates the soundness of our choice of judge model.\n\n**Table:** Zero-shot text-to-image performance with different judge models \n| Judge Models | GenEval (Overall) | T2ICompbench (Avg) | DPG-Bench |\n|-|-|-|-|\n|Qwen2.5-VL-32B|0.84|70.2|85.02|\n|InternVL2.5-26B|**0.86**|**72.7**|**85.57**|\n\nOf course, we are also considering adopting GPT-4o as the judge model, which could deliver more accurate assessments of generated images. However, the associated API costs exceed our present budget, so we defer this to future work.\n\n&nbsp;\n\n> **(II) Weakness2:** The qualitative results focus mostly on relatively simple image editing cases. It is unclear how well the model performs on more challenging tasks, such as multi-object editing.\n\n**A2:** We apologize that the qualitative results showcase relatively simple image-editing cases in Fig.4; our intention was to highlight that our model can consistently generate images resembling the source images while remaining coherent with the instructions during multi-turn editing. To demonstrate its ability to handle complex tasks, we curated 80 challenging test cases for image editing, including multi-object editing. We conduct a rigorous human evaluation, where human evaluators rate each generated image on an overall 1-to-5 scale, jointly weighing diverse dimensions including fidelity preservation and instruction following.\n\nAs shown in the following tables, **our model exhibits superior performance to open-source baseline methods for human evaluation of image editing** (only marginally underperforms GPT-4o), which demonstrates the strong performance of our model. In the next version of our paper, we will provide additional qualitative examples of these more challenging tasks, including multi-object editing.\n\n**Table:** Human evaluation on challenging test cases for image editing\n|Model|Human Score$\\uparrow$|\n|-|-|\n|InstructPix2Pix|2.4|\n|MagicBrush|2.1|\n|Ultraedit|2.8|\n|SeedX-Edit|2.6|\n|EditAR|2.5|\n|GPT-4o|**3.8**|\n|**Ours**|***3.2***|\n\n&nbsp;\n\n> **(III) Question1:** Does the proposed method compromise performance on visual understanding tasks?\n\n**A3:** Thank you for the insightful comment!  In fact, our method achieves synergistic gains across both visual generation and understanding tasks, thereby realizing an enhancement in visual understanding capabilities. To address your concern, we will provide detailed experimental results from three perspectives: 1) introducing a fine-grained image understanding experiment, i.e., visual entailment task, to validate the synergistic gain for visual understanding; 2) regarding Janus-Pro-R1 as an image-text semantic alignment evaluator to demonstrate the enhanced visual understanding; 3) Task Janus-Pro-R1 with describing the reasons for semantic misalignment in specific text–image pairs, further validating its enhanced visual understanding capability.\n\nBefore presenting the experiments, we first clarify the above evaluation setup. In Meta-Morph, the synergy between visual understanding and generation is concretely demonstrated by showing that mixed training on visual-generation data (e.g., text-to-image) and visual-understanding data (e.g., VQA) jointly improves both tasks. In our work, the continued training of Janus-Pro does not incorporate any dedicated visual-understanding data, such as VQA. Consequently, performance on several VQA benchmarks remains unchanged or even decreases slightly, which aligns with the inherent principles of model training. Our training essentially performs joint optimization of the text-to-image generation task and the visual-understanding task of evaluating image–text consistency. Guided by the rationale underlying Meta-Morph’s conclusion, we therefore select the image–text semantic-consistency task as the basis for our visual-understanding evaluation.\n\n**(3.1) Visual entailment task on SNLI-VE**\n\nTo validate that our method enhances visual understanding, we first conduct a zero-shot evaluation on the SNLI-VE benchmark.SNLI-VE is a classical benchmark for the visual entailment task, where each sample consists of an image (as the premise) and a text (as the hypothesis). The model is required to understand the fine-grained semantics in the image and determine whether the text can be entailed by the visual premise.\n\nAs shown in the following table, **Janus-Pro-R1 achieves a significant improvement of 20.8 points compared to its backbone model Janus-Pro**. The result indicates that our proposed method also improves visual understanding, achieving a synergistic gain through the unification of understanding and generation.\n\n**Table:** Zero-Shot Accuracy on SNLI-VE benchmark\n\n|Method|Accuracy(\\%)$\\uparrow$|\n|-|-|\n|Janus-Pro|35.2|\n|Janus-Pro-R1 (Ours)|**56.0**|\n\n&nbsp;\n\n**(3.2) Image-text semantic alignment evaluating**\n\nTo further demonstrate the enhanced visual understanding capability of our model, we conduct an additional experiment on the GenEval benchmark. We first instruct a text-to-image model to generate over 2,000 images for GenEval. Then we employ Janus-Pro-R1 and Janus-Pro as the evaluation functions for assessing text-image semantic alignment, comparing their results with those of the standard evaluation framework. More details are given in Section 4.3.\n\nAs shown in the following table, Janus-Pro-R1 achieves an 81.1% consistency ratio with the standard framework’s assessment, **exhibiting much higher consistency ratios compared to Janus-Pro**. This indicates that Janus-Pro-R1 more closely aligns with the standard metrics within GenEval, achieving greater evaluation precision and demonstrating stronger visual comprehension ability.\n\n**Table:** Consistency ratio compared with standard assessment in GenEval\n\n|Method|Consistency Ratio(\\%)$\\uparrow$|\n|-|-|\n|Janus-Pro|73.3|\n|Janus-Pro-R1 (Ours)|**84.2**|\n\n&nbsp;\n\n**(3.3) Image-text misalignment reason description**\n\nFinally, we collect several hundred pairs of semantically mismatched text-image pairs. We require Janus-Pro-R1 and Janus-Pro to determine whether the semantics of the image and text are matched and to provide relevant reasons. We then use GPT-4o as the evaluation model to score the reasons on a 0-to-1 scale (0 = unreasonable, 1 = reasonable).\n\nAs shown in the table below, Janus-Pro-R1 surpasses Janus-Pro-7B by producing much more reliable reasons of text-image misalignment, further demonstrating the model’s powerful image–semantic understanding capability.\n\n**Table:** Reason reliability score evaluated by GPT-4o\n\n|Method|Reason Reliability Score$\\uparrow$|\n|-|-|\n|Janus-Pro-7B|0.77|\n|Janus-Pro-R1-7B (Ours)|**0.91**|\n\n&nbsp;\n\n> **(IV) Question2:** What is the typical value of T used during training? Additionally, how does the distribution of $K_i$ evolve as training progresses?\n\n**A4:** As Janus-Pro does not have a particularly large context window, we set **T=3**, so the model activates at most two aha moments and thus generates at most three images in a single inference pass. \n\nDuring RL training, we encourage the model to satisfy the prompt **in as few rounds of image generation as possible**: **the fewer rounds it needs to produce a correct image, the larger the reward we provide.** For instance, if the model can generate a compliant image and perform an accurate self-evaluation in the first round, it could receive the maximum reward score.\n\nUnder this reward scheme, **the proportion of cases with K=1 gradually increases, while the proportion of K=2 and K=3 (especially K=3) steadily declines.** Specifically, the distribution shifts from approximately (K=1): (K=2): (K=3) ≈ 70: 18: 12 at the start of training to (K=1): (K=2): (K=3) ≈ 84: 11: 5 by the end of training.\n\n&nbsp;\n\nThank you once again for your valuable suggestions! We will incorporate the experiments into our revised manuscript."}, {"review_id": "x0vfzfYVKi", "reviewer": "Reviewer_oSZo", "summary": "The paper proposes to endow Any-to-Any Multimodal LLMs with a capacity to reason before generating images, thereby harmonizing the reasoning capacity in image-to-text understanding with text-to-image generation ability of the same MLLM. Such Chain-of-Thought-based image generation unlocks synergy between image-to-text and text-to-image capacity by allowing iterative feedback and refinement during the image generation process. Starting from a pretrained Any-to-Any MLLM (Janus-Pro), standard recipe of SFT(Supervised Fine-Tuning)-then-RL(Reinforcement Learning) is applied to ellicit CoT-based image generation capacity. For  the SFT stage training, a new training data is collected by generating multiple images given the same prompt and evaluating text-image consistency to support the verificartion and refinement capacity. For RL two reward types are considered: the generation reward for improving the image quality and the comprehension reward for enhacing the verification performance. For editing, the rewards are further divided for update of the edits and preservation of the original features. The final model is shown effective at compositional image generation, image editing and image semantic evaluation tasks.", "questions": "1. How would the performance change if one trains image-to-text and text-to-image in separate modules, while initializing the both with the same Janus-Pro weight and train jointly with reinforcement learning? I am curious whether learning to verify leads to better refinement and vice versa. Since this will require extensive ablation study, I am not suggesting to do this in the rebuttal period. Still, can you provide your view on how this intervention will change the outcomes?\n2. Is there a step-by-step evaluation result? It would help to see the evaluation results for each generated image_k, k \\in [1, … K] in the CoT process.\n3. As I suggested above, I think explanation on how this differs from zero-shot CoT-based image generation models [1] or iterative update without verification [2].\n\n[1] https://arxiv.org/pdf/2402.12741\n[2] https://arxiv.org/pdf/2410.07171: ICLR 2025", "limitations": "Yes", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Originality: Most previous literature on compositional image generation connect the text-to-image and image-to-text modules or functions in zero-shot manner without a goal to analyze potential synergy between the two. This work not only provides a working implementation of training such unified model, but also analyzes the synergy between each component thoroughly. \n\nSignificance: I think synergy between image-to-text and text-to-image is essential in supporting necessity of training the two tasks in a single model. The reasoning-based image generation setup presented in this paper provides a compelling example on the direction: the verification (text-to-image) and refinement (image-to-text) are effectively interleaved for better quality in the downstream task.\n\nClarity: I found the presentation clear and easy to follow. It would further help gauging the contribution of this work within the current scientific progress to compare the difference with previous compositional and iterative image generation methods.", "was_revised": true, "final_justification": "I was generally satisfied with the contribution and presentation of the original draft. My main concern was the lack of an extensive ablation study, which the authors have acknowledged and committed to address with additional experiments. Accordingly, I am raising my score.", "rebuttal": "We sincerely thank you for your insightful feedback. We are encouraged by your strong endorsement of our work in terms of Originality, Significance, and Clarity. We will explain your questions point-by-point.\n\n> **(I) Question1**: How would the performance change if one trains image-to-text and text-to-image in separate modules?\n\n**A1:** Thank you for the insightful question! We agree that this is an interesting direction worth further exploration. We will present our view on how this intervention will alter the outcomes.\n\n**(1.1) Disrupting CoT without genuine deep thinking**\n\nOur method integrates visual comprehension (verification) and visual generation (refinement) within a single reasoning trajectory, allowing the model to perform iterative refinement through self-reflection in a flexible and self-driven manner. \n\nWhen verification and refinement are separated into different models, **the coherent reasoning chain is disrupted. It may fail to genuinely drive the MLLM’s deep reasoning and introspection for image generation.** For the two Janus-Pro models, only the output of one model is passed as input to the other, without preserving the intermediate hidden states that encode crucial reasoning processes. As a result, the Janus-Pro for text-to-image may not genuinely comprehend the image flaws and revision suggestions provided by the Janus-Pro for image-to-text, so the subsequent refinement process may inadequately address the issues identified during verification.\n\n**(1.2) Lacking synergy and mutual enhancement between verification and refinement**\n\nIn our method, visual comprehension (verification) and visual generation (refinement) are optimized together in a unified framework and reinforce each other in a synergistic manner. Improved comprehension helps guide more precise generation, while high-quality generation provides richer visual semantics that support better comprehension. This mutual enhancement enables the model to progressively conduct higher-quality verification and refinement. \n\n**In contrast, a decoupled framework with separate modules lacks this synergy, as each module operates in isolation with limited capacity to benefit from the improvements of the other.** As a result, the overall performance may fall short of that achieved by a unified architecture, even though optimizing the two separate modules demands substantially greater training resources.\n\n&nbsp;\n\nOf course, the above analyses will require extensive ablation studies for empirical validation, which we will rigorously conduct after the rebuttal period, and we expect the results to further reveal both the necessity and benefits of unifying image-to-text and text-to-image within a single model. Furthermore, we also regard the mixture-of-transformer (MoT) design introduced in [1] as a highly promising solution: it interlinks the attention functions of the text-to-image and image-to-text modules, ensuring that the two capabilities remain unified within one model while still preserving independent parameters. We view this as a high-ceiling improvement. However, as noted in [1], it demands much more computational resources for training, so we leave its exploration to future work.\n\n*Reference:*\n\n*[1] Emerging Properties in Unified Multimodal Pretraining.*\n\n&nbsp;\n\n> **(II) Question2**: Is there a step-by-step evaluation result? \n\n**A2:** Thank you for your question. We conduct a step-by-step evaluation on GenEval to demonstrate the image quality in the CoT process. Specifically, we evaluate the generated images $I_1$, $I_2$, and $I_3$, corresponding to the first, second, and third rounds of generation, respectively (the maximum round T=3). We set $I_3=I_2$ ($I_3=I_2=I_1$) if the model performs only two rounds (one round) of image generation in a single inference.\n\nAs shown in the following table, **our method exhibits consistent performance improvements as the number of reasoning steps increases in the CoT process.** This indicates that our method effectively leverages self-reflection within the long CoT process to iteratively refine the generated images, enabling the model to correct flaws and enhance visual quality through deeper reasoning.\n\n**Table:** Step-by-step GenEval performance of intermediate images $I_k$ generated during the CoT process.\n\n|Image $I_k$|Overall|SingObj|TwoObj| Counting |Color|Pos| ColorAttr |\n|-|-|-|-|-|-|-|-|\n|$I_1$|0.83|0.99|0.93|0.60|0.89|0.82|0.74|\n|$I_2$|0.85|0.99|0.93|0.62|0.91|0.89|0.75|\n|$I_3$|**0.86**|**0.99**|**0.94**|**0.66**|**0.92**|**0.90**|**0.80**|\n\n&nbsp;\n\n> **(III) Question3**: Explanation on how this differs from zero-shot CoT-based image generation models or iterative update without verification.\n\n**A3:** Thank you for your insightful comment! Though our method has a similar iterative optimization paradigm with the zero-shot CoT-based methods and iterative update methods without verification, there are still key differences that distinguish our research from the following three perspectives:\n\n**(3.1) Architecture Advantages: Lightweight Design and Self-Driven Reasoning with Genuine CoT**\n\nBoth zero-shot CoT-based image generation models [1] and iterative update without verification [2] typically require assistance from external models, and are hard to perform iterative self-correction in a self-driven manner.\n\nSpecifically, MuLan [1] relies on proprietary MLLMs to provide global guidance for image generation, and uses VLMs to decide whether to redraw after generating each object. IterComp [2] introduces a trainable reward model separate from the diffusion model and performs iterative training of both models, making the system complex and heavy. This forced coupling of two distinct models for iterative image generation, **not only fails to genuinely drive the MLLM's deep reasoning, but also results in a relatively heavy framework with limited flexibility.** \n\nIn contrast, our method unified the verification and refinement within a single model, bringing two architecture advantages: **(1)** it allows a **genuine CoT** to emerge spontaneously from the model’s deep thinking, naturally collaborating its visual comprehension and generation capabilities into an interleaved image-text reasoning chain; **(2)** it requires no external models, adopting a **lightweight and elegant design** that offers **greater flexibility** than the two aforementioned approaches.\n\n&nbsp;\n\n**(3.2) Synergistic Gains from Unified Comprehension and Generation**\n\nWhile both methods ([1] and [2]) aim to improve image quality via iterative refinement, they still treat visual comprehension (image-to-text) and visual generation (text-to-image) as **two disjoint processes**, **overlooking the synergistic gains** that arise when visual comprehension and generation are co-optimized within a unified model. For instance, MuLan [1] focuses almost exclusively on refining the visual generation pipeline, thereby neglecting the critical contribution of visual comprehension to image quality. Similarly, although IterComp [2] updates its reward model during training, the optimization of the reward model remains decoupled from that of the text-to-image generator.\n\nWhile in our method, **unifying visual comprehension (verification) and visual generation (refinement) within a single model can lead to mutual enhancement**, as discussed in prior work [3, 4]. From the comprehension side, a better understanding of visual semantics, such as object identity and spatial layout, helps guide more faithful and coherent image generation. From the generation side, generating visual tokens step by step forces the model to learn detailed visual structures, which in turn improves its ability to comprehend images. As demonstrated in Sections 4.1 and 4.3, **our approach delivers synergistic gains that render Janus-Pro a superior image generator and image-semantic verificator.** Additionally, the visual comprehension and generation capabilities are naturally linked to form an interleaved text-image reasoning chain under the spontaneous scheduling of the MLLM, which can be treated as a CoT that truly helps produce more refined images.\n\nIn contrast to our unified method, both zero-shot CoT-based methods and iterative update methods without verification treat comprehension and generation as disjoint processes. As a result, **they are unable to benefit from the synergy to enhance generation quality, ultimately constraining their capacity, especially in complex scenarios.**\n\n&nbsp;\n\n**(3.3) Superior Performance in Image Generation Task**\n\nTo better understand the practical impact of these methods, we evaluate two baselines, MuLan [1] and IterComp [2], on the GenEval benchmark. As shown in the following table, **Janus-Pro-R1 significantly outperforms both methods**. Specifically, it surpasses MuLan by 0.34 and outperforms IterComp by 0.18 in overall score. This indicates that our proposed method achieves better image generation performance than zero-shot CoT-based methods and iterative update methods without verification.\n\n**Table:** Performance comparison on the GenEval benchmark.\n\n|Method|Overall|SingObj|TwoObj|Counting|Color|Pos|ColorAttr|\n|-|-|-|-|-|-|-|-|\n|MuLan-SD v1.4|0.38|0.96|0.41|0.15|0.28|0.34|0.14|\n|MuLan-SDXL|0.52 |0.98|0.51|0.17|0.72|0.45|0.30|\n|IterComp|0.68|0.97|0.85|0.63|0.86|0.33|0.41|\n|Janus-Pro-R1 (Ours)|**0.86**|**0.99**|**0.94**|**0.66**|**0.92**|**0.87**|**0.78**|\n\n&nbsp;\n\n*Reference:*\n\n*[1] MuLan: Multimodal-LLM Agent for Progressive and Interactive Multi-Object Diffusion*\n\n*[2] IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation*\n\n*[3] SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation*\n\n*[4] Show-o: One Single Transformer to Unify Multimodal Understanding and Generation*\n\n&nbsp;\n\nOnce again, we are grateful for your valuable suggestions, which have significantly contributed to improving our paper!"}]

Author rebuttal:

Meta-review: The reviewers generally found the paper to be well motivated, clearly written, and technically sound, with a novel framing of text-to-image generation as a multi-round editing process guided by reasoning chains. They highlighted the strong empirical performance across multiple benchmarks, including state-of-the-art or competitive results on compositional T2I tasks and improved trade-offs in image editing. Reviewers also appreciated the thorough ablations on model components and training setup, as well as the broader potential of the method to unify image generation and editing within multimodal LLMs. While some reviewers noted concerns about reward model reliance, synthesized training data, computational overhead, and relatively modest or uneven improvements in some evaluations, the overall consensus leaned positive, with one strong accept and multiple borderline accepts.

During the rebuttal, the authors provided additional ablations and human evaluations that helped address key concerns around the reliability of results and practical impact. Clarifications regarding judge model choices, performance on harder editing cases, and the role of task synergy strengthened confidence in the method’s soundness and generality. Given the overall novelty, solid empirical results, and clarified responses, the final recommendation is accept.
Decision: Accept (poster)