Vinci: Deep Thinking in Text-to-Image Generation using Unified Model with Reinforcement Learning
Authors: Wang Lin
Wentao Hu
Liyu Jia
Kaihang Pan
Zhang Majun
Zhou Zhao
Fei Wu
Jingyuan Chen
Hanwang Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Image generation; Image understanding
Primary area: applications

Abstract: With the continuous development of large language models and reasoning chain technologies, the potential of deep reasoning based on reinforcement learning has shown remarkable promise in multi-task scenarios.  However, existing unified models have yet to achieve end-to-end integration in image generation and understanding tasks, limiting the model’s self-reflection ability and the realization of cross-modal reasoning chains.  To address this, we propose Vinic, a novel framework designed to enable interleaved image generation and understanding through deep reasoning capabilities.  We leverage a small amount of multimodal chain-of-thought (MCoT) data for cold-start and employ reinforcement learning to guide the integration of image generation and understanding tasks.  Additionally, we introduce a momentum-based reward function, which dynamically adjusts the reward distribution by considering historical improvements, ensuring the stability of the model across multiple generations.  Experimental results demonstrate that integrating MCoT can achieve a +22% improvement over the base model on Geneval, effectively enhancing both image generation quality and instruction alignment capabilities.

Vinci: Deep Thinking in Text-to-lmage Generation using Unified Model with Reinforcement Learning
Wang Lin Wentao Hu?.*, Liyu Jia", Kaihang Pan', Majun Zhang', Zhou Zhao', Fei Wu' Jiigyuan Chen 1i, Hanwang Zhang? Zhejiann University. "Nanyang Technological University
Linwanglw@zju. edu. en, jingyuanchen02: edu.cn en
Abstract
With the continuous development of large language models and reasoning chain technologies, the potential of deep reasoning based on reinforcement learning has shown remarkable promise in multi-tass task scenarios. However, existing uni- fied models have yet to achieve end-to-end integration in image generation and understanding tasks, limiting the model's self-reflection ability and the realiza- tion of cross-modal reasoning chains. To address this, we propose Vinci, a novel framework designed enable interleaved image generation and understanding through deep reasoning capabilities. We leverage a small amount of multimodal saain-of-though (MCoT) data for cold-start and employ reinforcement learning guide the integration of image generation and understanding tasks. Additionally, reward function, which dynamically adjusts the
we introduce a momentum-basee reward distribution by considering historical improvements, ensuring the stabil- ity of the model across multiple generations. Experimental results demonstrate that integrating MCoT can achieve +22% improvement over the base model on Geneval, effectively enhancing both image generation quality and instruction alignment capabilities.
Introduction
With the rapid development of large language models 9 82 61
and Chain of Thought 449 37 75
2
(CoTT) techniques, deep thinking through reinforcement learning has become key research focus in artificial intelligence. It has shown exceptional reasoning capabilities in tasks like mathematical problem-solving 622 and code generation
29 28 61). significantly improving both model
performance and interpretability by providing clear, step-by-step deduction and vrcision-making
This step-by-step reasoning ability is being applied to cross-modal tasks, particularly in image generation and understanding. In image understanding, many structured reasoning mechanisms [5] have been proposed to enhance controllability and interpretability. In image generation, [10
79
14 57 focuses on refining the input prompts or employing text-based layout construction prior to synthesis. Recently, breakthroughs in unified models have enabled a single model to simultaneously perform image generation and understanding. However, the potential of Chain of Thought techniques existing unified models has yet be fully explored.
The main challenge lies in the lack of end-to-end integration between image generation and under- standing. For example, in models like Janus-Pro [5] and Show-o (78), images are represented in two ways within the model (i.e., the model cannot directly understand the representations of the images generates), thus limiting the model's ability to reflect and optimize the generated results. Further- more, while there have been attempts 89 31 22 to introduce CoT into these models, they are mostly limited to unimodal reasoning chains. For instance, in the T2I-R1 (31) model, semantic-level CoT is applied only to enhance text prompts, while token-level CoT is optimized only on image tokens.
39th Conference on Neural Information Processing Systems (NeurIPS 2025).

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Text
Seractiue mour e plattc Jncue Mikee
Sewers of brown kof's and dOLT.
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Image
Image
(b) Unimodal-CoT
(6) Muttimodal-CoT
Figure 1: The illustration of CoT in text-to-image generation, showing three different approaches: (a) Direct-IO, where the image is generated directly from the prompt, (b) Unimodal-Con which involves generating an extended prompt before producing the image, and (c) Multimodal- -oTT, where both text and image are processed iteratively to refine the image generation process based on the evolving understanding of the prompt.
This unimodal approach to reasoning chains restricts the model's ability to perform cross-modal deep thinking, further limiting its performance improvements.
Our goal is to integrate interleaved Multimodal Chain-of-Thought (MCoT) capabilities into the model, allowing it to observe and reflect on its generated results in real-time, thus achieving deep integration of image generation and understanding. As shown in Figure I initially, the model generates an image from the prompt "a brown knife and blue donut". Upon reviewing the first image, the model identifies that the donut is not blue and the knife is missing. It reflects on these issues and adjusts the generation strategy to correct the donut's ' color to blue and adds knife, but the knife's 's color and shape still do not match typical expectations. Realizing the mistake, the model gives the knife's appearance description. This iterative process reflects the deep thinking of image generation and understanding, refining the generated images based on real-time evaluation and adjustments.
To achieve this, this paper introduces Vinci, which is the first image generation model capable of deep thinking. Specifically, we employ a progressive training strategy and fine-tune the model using small amount of MCoT data to provide initial CoT guidance. Subsequently, we use reinforcement learning to guide the model to simultaneously focus on both image generation and image under- standing tasks during the generation process. In addition to applying the reward model to evaluate the generated images, we introduce a momentum reward function to further enhance the model's stability and generation quality. The core of this method lies in dynamically distributing process rewards by considering the improvements made by the model in historical generation processes. By employing this approach, the Vinci can better balance the model's immediate performance with its long-term stability, preventing performance fluctuations or degradation over multiple iterations. Our contributions are summarized follows:
We propose Vinci, the first image generation model capable of deep reasoning, which integrates interleaved MCoT capabilities. This enables the model not only generate images but also observe and reflect on its outputs in real-time.
We propose a momentum-basee reward function that dynamically distributes process rewards based on historical improvements. This approach enhances the model's stability while balancing immediate performance with long-term stability.
Experimental results demonstrate that integrating MCoT can achieve a +22% improvement over the base model on Geneval 17 benchmarks.
2
Related Work
2.1 Unified Generation and Understanding LMM
Recent advancements in multimodal understanding and image generation have led to efforts to integrate these tasks into unified framework. The main approaches can be categorized into two

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types. (1) Fused autoregressive and diffusion models such as Transfusion (88siionn8888,. Show-o [78), and MonoFormer [86), combine the strengths of autoregressive models for text generation with the high- quality image generation of diffusion models. Text is generated autoregressively. while images are produced through a multi-step denoising process. balances symbolic control with visual fidelity, but fails to perform generation and understanding simultaneously. (2) Autoregressive models unify image and text generation within a sequential framework, using powerful autoregressive language models (e.g., LLaMA D. Vicuna [2]) at their core. These models encode images into discrete visual tokens and process them alongside text tokens. Various image tokenization strategies, including pixel-based like LWM [50), Chameleon [65], and ANOLE (8), semantic-basee like Emu (64]. VTT [35), and DreamLLM [12,, and learnable query encoding like SEED [15] and MetaQueries [55], allow the integration of visual information within the autoregressive framework. These autoregressive models demonstrate significant improvements in both image generation quality and efficiency. Note, we aim to perform generation and understanding simultaneously. Thus, the autoregressive models such as Chameleon [65] and Emu3 [71], which have a unified image representation, provide the foundation for MCoT in image generation.
2.2 Multimodal-CoT Reasoning
Multimodal Chain-of-Thought (MCoT) has gained significant attention in tasks such Visual Question Answering (VQA), where early works like IPVR 66] and Multimodal-Cor [85] laid the foundation by generating intermediate rationales before final predictions. Subsequent research im-
proved MCoT by introducing self-consistency [72] with word-level majority voting and dynamic reasoning selection based on human cognitive strategies. Frameworks like CoCoT 84 and Rela- tionLMM (77 enhanced multi-image comprehension and object relationship modeling. Methods such as DDCoT 87 and Socratie Questioning [25] employed staged reasoning for better interpretability, while Chain-of-Spot [51] and DCoT (30 focused on region-of-interest analysis to improve contextual understanding, Recent advancements expanded MCoT beyond VQA. Techniques like G-CoT 152) and STIC M addressed data annotation limitations through automated augmentation, and DPMM- CoT 24 regenerated image features from the latent space to handle complex reasoning. Additionally, multimodal rationales,
in Visual-Col [59] and MVoT [39], have enhanced reasoning across modalities, improving overall model performance. In image generation tasks, models like Prompt- CoT [83 and Lay0utLLM-T21 (57) optimize input prompts and layout construction, respectively, to improve image synthesis quality. These developments highlight the growing importance of MCoT in multimodal reasoning, enabling models to tackle complex image-related tasks with greater efficiency and interpretability.
3 Method
3.1
Preliminary
Recently, reinforcement learning has become the primary method for unlocking the reasoning capa- bilities of large language models (LLMss.. [62] introduces the Group Relative Policy Optimization (GRPO) framework. Unlike reinforcement learning algorithms such as PPO [60] that require critic model to evaluate policy performance, GRPO compares groups of candidate responses directly, climi- nating the need for an additional critic model. Given the input instruction q. GRPO first generates G distinct predictions {or, ....... .... 0y} from the old policy Toma. Then, GRPO takes actions based on these predictions and denotes the obtained rewards as {ri. r2, ..., rg) By computing their mean and standard deviation for normalization, GRPO determines the relative quality of these responses:
A; T; mcan mcan((r).............. (TII....
(rd(ri)...,rg)))
where A, represents the relative quality of the i-th answer. The GRPO method employs a clipped objective function, similar PPO, and introduces KL penalty term that compares the current policy Ty with the reference model To. into the loss, as follows:
led
Σ Σ
(0) = - G
(2)
1=1
Inin (ri,(0)AAn (ri. e, +e) ALc) AcL) -3IDx kr. (Tol|Ted)
(3)
3

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(a) Cold-Start with MGoT
For Deep Thinking
beca Reword
Unitied Auto-Regressive Model
Unitied Reto-Regreseive Model
Frecuss Romond
Taken
1thh
Image
Toxt Token
(i+ 1) Image
Boced Rewerd
Prompt:
Generate photo of brown knife and blue donut.
Prompt
Figure 2: Overview of the Vinci model architecture. (a) Cold-start for long Multimodal Chain-of- Thought (MCoT), where the unified autoregressive model sequentially processes text tokens and image tokens. (b) Reinforcement Learning (RL) for deep thinking, where the model incorporates multiple reward signals (program- program-baseed, momentum process, and QA D--bdsed rewards) to iteratively refine the generated content during the generation process.
where Ti,)(0) represents the ratio between the probabilities of TT and Tee for generating the current token:
(0) Ta TT(Oo,e99 Oi,ce)
Tiw (ot,1 q, oi,ct)
In text-to-image generation, the model is guided follow predefined template to produce the reasoning process and final images. The reward functions evaluate the generated image in visual RL and provide feedback for optimization.
3.2 Cold Start with Multimodal-CoT
we aim incorporate long-chain of-thought reasoning into the unified models generation process as shown in Figure 2 However, existing unified models are typically designed to generate images single forward pass, lacking the ability to perform multi-round understanding and reflection in the image generation process. We first introduce cold-start stage to adjust the model's output format and prevent instability of RI. training during early-stage.
Inspired by recent advances in reasoning models 19
Multimodal-CoT Data Collection. Unlike Unimodal-CoTi which only refines the prompt without considering the generated image, the Multimodal-CoT we introduce refers to process in which the model engages understanding and reflecting on intermediate images during text-to-image genera- tion. In each generation attempt, the model analyzes the generated image, identifies inconsistencies or areas for improvement, and proposes adjustments to better align with the prompt and enhance visual quality. This iterative, interleaved process of textual reasoning and visual feedback ultimately leads to the generation of the final, high-quality image.
To construct Multimodal-CoT data for the cold-start stage, we design a three-stage data pipeline.
Generate the Image for MCoT. For each text query we generate a set of n candi- date images, denoted as [p1, P2,.-., P..) We then apply object detection model to each image to identify objects and obtain detection outputs, resulting in a set of tuples {(p1, 01) (P2, 02), .., (Pn, o,)), where o; represents the detected objects in image p;-
Generate the Text for MCoT. To prevent confusion in multi-image understanding, first require the multimodal large model (MLLM) to independently evaluate each generated tuple (ps, for its semantic alignment with the original query and overall quality. Note that we also provide the output of the object detection model o,, which helps reduce hallucinations in the multimodal large model. In this process, MLLM generates caption e; and a score 9i for each image p4. These outputs are then combined into annotated triples (p;, Cts 9:), which ((11, C1, 91), (p2: C22 92), (Pn.Cn,
sorted by their scores in ascending order, resulting in the final set
Gu, }n where 91 & 92 S S 9n-
MCoT Construction: Given predefined reasoning step count k. we randomly select triples from this sorted set for combination. It is important maintain the relative order

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of the triples according to their scores and ensure that each combination contains at least one high-scoring (correct) triple, which serves as the endpoint for the MCoT. This results in combinations of the form {(p,, ct g.)..... (p ct 9.) )] where k S n. Each combination is then input into the MI..M to construct the final MCoT data.
For the final constructed MCoT, we use GPT-4o [53] to evaluate and filter out data that contains hallucinations or failures at the MCoT endpoints. Through these three stages, we construct an interleaved multimodal sequence that represents iterative process of generation and reasoning.
3.3 Reinforcement Learning For Deep Thinking
Reward Functions Based on Comprehension Models The overall design philosophy of our reward model is to utilize comprehension models to evaluate both the generated image and text in RI. In this paper, we categorize the comprehension-base reward into two major types:
Program-based Reward Function. For the generated image, the comprehension model should understand and evaluate the consistency between the generated image and the textual prompt. Given the prompt like "3 clocks and I dog" and generated image, we can use visual detectors 44 to evaluate the generation quality. For example, we count the clocks based on the detector's confidence, returning 1 if the count is correct and 0 otherwise. Each prompt has its own item sets to be tested, and the average of the scores for each test is used as the reward score Rd.
QA-based Reward Function for Generated Text. For the generated text, the comprehension model should evaluate whether the text accurately describes the generated image. Given both the generated image and text, like Figure (c), we leverage the Multi-modal Large Language Models (MLLMs) to judge the quality of the evaluation text for the generated image. We require the model to be evaluated on three aspects: the completeness of the image description, whether it identifies issues in the image, and whether it includes strategies for improvement. Each aspect rated on a scale of 0, 1, or 2, with the final reward score R₂ being the average of the three scales.
Momentum Process Reward Function Given sequence of n generated images {-+)==1 let 8, E [0, 1] denote the quality score of the f-th image produced by the reward function. We aim to design process reward function Rn, that simultaneously evaluates both instantaneous quality and improvement dynamics.
Inspired by the Adam optimizer 36), we propose dynamic process reward that is used to encourage consistent improvements as follows:
Rm 1 R
(()s +
Σ
A VV k=
--1
(5)
+6
where the hyperparameter a balances between absolute quality and improvement momentum, the time decay factor À E 10, imposes attenuation later improvements, and e 10-8 ensures numerical stability.
The current improvement A max(0. (sx)) - Rd (sx--))) )), quantifies non-negative increments between generated images. With the momentum normalization term Av/VV,, which establishes dynamic scaling based on historical improvement patterns. This reward function achieves three objectives. First, it normalizes current improvements against historical volatility, preventing dis- proportionate rewards from isolated quality spikes. Second, the exponentially weighted variance calculation prioritizes recent trends while maintaining memory of long-term patterns. Third, the non- linear response curve generates superlinear rewards when sustained improvements exceed historical baselines (A, > VVR), while penalizing inconsistent progress through variance accumulation.
The historical volatility Vi 7V--1 + (1 A). evolves through exponential moving averaging. The momentum decay rate E 1) controls historical variance adaptation. This formulation ensures older improvements contribute diminishing weights the variance estimate, creating adaptive resistance to quality fluctuations.
The --1 term implements exponential temporal decay to prioritize early-stage improvements, modeling the empirical observation that establishing baseline quality early enables more effective subsequent refinements. The decay schedule follows * (n which creates implicit temporal milestones where early improvements receive more weighting, while late optimizations
5

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Type
Method
Single Obj.
Two Obj.
Counting
Colors
Position
Color Attr.
Overall
PixArt-o
3
0.98
0.50
0.44
0.80
0.8
0,70
0.48
SDXL. [5
0.98
0.74
0.39
0.85
0.15
0.23
0.55
FLUX,1-der
38
0.98
0.79
0.73
0.77
0.22
0,45
0.66
Gen. Only
DALL-E3 63
0.87
0.83
0.45
CogView4-66
0.99
0.86
0.66
0.79
0.48
0.58
SD3-Medium
[K]
0.99
0.94
0.72
0.89
0.33
0.60
0.74
SEED-X [16
0.97
0.58
0.26
0,80
0.19
0.14
0.49
Emu3-Gen 71
0.98
0.71
0.34
0,81
0.17
0.21
0.54
TokenFlow-XI
58
0.95
0.60
0.41
0.81
0,16
0.24
0.55
Transfusion 88
0.63
D-DiT 41
0.97
0,80
0.54
0.76
0.32
0.50
Unified
Show-o 78
0.80
0.66
0.84
0.31
0.50
0.68
ILLUME+ 26
0.99
0.88
0.62
0.84
0.42
0.53
0.72
Infinity 23
0.85
0.49
0.57
0.73
lamus-Pro-7P
5
0.99
0.89
0.59
0.90
0.79
0.66
0.80
GPT-4o 53
0.99
0.92
0.85
0.91
0.75
0.66
0.85
Vinci(Ours)
0.99
0.86
0.48
0.83
0.86
0.54
0.76
Table 1:
Evaluation of text-to-image generation ability on GenEval benchmark. Emu3-Ges is our base model. Gen. Only indicate models that can only do generation tasks, Unified. indicate models that can do both generation and understanding tasks
contribute minimally to the momentum score. The decay rate in À controls the transition steepness between these regimes.
To effectively guide the model's learning process and ensure that it can generate high-quality outputs, we combine the three reward functions as:
= R2 + Rs Rm
(6)
By integrating these three components, the overall reward function leads more accurate, coherent, and consistent multimodal generation.
Experiments
4.1 Experimental Setup
Training Data. Our training data consists of two distinct phases. In the cold-start phase, we randomly generated 20,000 MCoT; the images generated by Flux (38) and the text is generated by Qwen- VL (68). Following this, the reinforcement learning (RL) phase began with the utilization of 30,000 prompts, all without corresponding images. Notably, to ensure the integrity of the training and evaluation processes, we performed careful deduplication of the training prompts, eliminating any overlap with the set. This measure was critical preventing data leakage and ensuring a robust evaluation of the model's performance unseen prompts.
Implementation details. For training, adopted Emu- Emnu-Gen the base model, which has unified image representation. The model's context length was extended to 15,360 tokens, which allowed for processing longer sequences of text and image pairs and supported approximately three iteration within each context window. The learning rate was set to le-5 and a beta of 0.01.
4.2
Main Results
The experimental results presented in Table highlight the performance of various models the GenEval [17] benchmark, which evaluates the text-to- eext-to-image generation ability across different tasks. The results highlight the superior performance of Vinci over the Gen. Only models, demonstrating the necessity of integrating comprehension abilities into generative models for enhanced instruction following capabilities.
In comparison with the Unified models, Vinci only lags behind Janus-Pro- indicating its strong competitiveness among models capable of both generation and understanding tasks. The enhancements in Vinci over the baseline model Emu3-Gen [71] are particularly significant across several dimensions. Specifically, Vinci elevates the Position generation score from 0.17 to 0.86
and GPT-4o 53

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Durs
GPT-4o
Janur-Pro-78
Med-Mediamm
CogVTeud--G
Infinity
Show
FLU
a photo tv remote below cow
Position
photo of apple
photo of dog right of teddy bear
Color Attribute
a photo of brown knife and a blue donut
photo of purple wine gloss and black apple
photo of red umbrella and blue couch
a photo of blue carrot
Colors
a photo purple bear
photo an orange laptop
photo of four apples
Counting
photo of four giraffes
a photo of three fridges
Figure 3: Qualitative experimental results of Vinci. Compared to existing text-to-image generation
models, the images generated by Vinci demonstrate better alignment with the given prompts.

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Method
Single Obj.
Two Obj.
Counting
Colors
Position
Color Attr.
Overall
Emu3-Gen 711
0.98
0.71
0.34
0.81
0.17
0.21
0.54
w:
0.98
0.72
0.31
0.77
0.51
0.30
0.59
w.R;
R.R.R. 0.96
0.73
0.36
0.75
0.72
0,44
0.66
0.99
0.84
0.44
0.79
0.75
0,46
0.71
w. w.R. &&R.&.&.........RR
0.99
0.86
0.48
0.83
0.86
0.54
0.76
Table 2:
Ablation study results on the GenEval benchmark. We compare the performance of the base model Emu3-Gen and its variants with modified reward functions.
(+0.69) and Color attribution score from 0.21 to 0.54 (+0.33). These improvements underscore the efficacy of the Multimodal- By enabling the model to observe and reflect its generated results in real-time, Vinci not only generates images but also enhances the interpretability and controllability of the generated content.
in achieving deep integration of image generation and understanding.
Furthermore, the results in Figure 3 show that Vinci demonstrates robust performance across variety of tasks. For instance, when tasked with generating an image of a "tv remote below a cow" or an "apple above tv" Vinci successfully positions the objects per the instructions, showcasing its ability to understand and execute complex instructions involving multiple objects.
4.3 Ablation Study
The ablation study results presented Table 2 provide valuable insights into the impact of different reward functions the performance of the Vinci model.
Image understanding ability can feed back into image generation. Initially, when the model is trained using only the text quality evaluation through Rg, we observe improvements in image generation, particularly in areas such as position and color attribution. This result suggests that improving the model's 's ability to understand the generated image can have positive effect on the image generation process itself.
Explicit image quality evaluation is important. By providing explicit feedback on the image quality, R; as direct optimization signal, guiding the model to produce higher-quality images. The explicit explicit evaluation of image fidelity ensures that the generated images are not only contextually accurate but also visually compelling. The model therefore able focus more on producing realistic and visually appealing images. The combined use of both image and text reward functions (w. &&&R,) results in additional performance gains.
Stable iteration process leads to further improvement. The final enhancement comes from the incorporation of momentum-basse baseu process reward function Rm, which takes into account historical improvements over time. This mechanism encourages the model to maintain a steady and consistent progression in its learning process. It draws parallels with human cognitive processes, where sustained, incremental progress is highly valued. By emphasizing gradual improvement, the momentum-bast reward ensures that the model continuously refines its understanding and generation abilities over time, rather than oscillating between high-performang peaks and low-performance troughs. This method not only leads enhanced model performance but also stabilizes the training trajectory. making the reinforcement learning process more reliable and robust in the long term.
4.4 Visualization of Multimodal-COT
The visualizations in Figure 4 demonstrate the adaptive nature of Vinci, which allows the model autonomously control the length of the reasoning process. This capability enables the model to directly produce the final image after single round of thought for simpler cases, while more complex scenarios may require two three iterations of contemplation to yield the desired output. When tasked with creating an image of a blue vase and black banana, the model's initial attempt missed the banana and had an unappealing vase shape. Recognizing these shortcomings, the model engaged in further reasoning to introduce the banana and enhance the vase's aesthetics. It continued this process until achieved an image that not only met the user's specifications but also exhibited high visual quality.
8

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Figure 4: Case study demonstrating the variable-length MCoT generation process. For each query, the model iteratively generates and refines images based on feedback from the reasoning process.
This variable-Iength reasoning approach enhances both the output's fidelity and the process's overall efficiency. By allowing the model to determine the appropriate depth of thought required for each task, ensures that while complex images receive the detailed consideration needed to meet high standards, simpler images are generated swiftly without unnecessary computational overhead.
5 Limitation and Future Work
There remains considerable room for exploration in the unified model for multimodal understanding and generation. Currently, the community lacks a truly general-purpose foundational model that can seamlessly integrate cross-modal understanding and generation capabilities. Although state-of-the-art models like Janus-Pro strive to balance these capabilities, they sacrifice a unified representation of understanding and generation at the foundational level. Additionally, the length of image tokens (which often requires hundreds more tokens to represent single image), combined with the limitations of the context window in autoregressive models, restricts the depth and efficiency of the model's 's reasoning capabilities. Future work can focus on improving both the understanding and generation capabilities of unified models, as well as developing better reasoning strategies to optimize efficiency and performance.
6 Conclusion
In this paper, we introduced Vinci, a novel image generation model that integrates deep reasoning capabilities through the use of interleaved multimodal Chain-of-Thouur (MCoT). Unlike previous models that primarily focus on either image generation or understanding, Vinci achieves a unified approach that combines these two tasks in a way that allows the model to reflect on and iteratively improve its generated outputs in real time. Our method leverages reinforcement learning, where the model learns to refine its output based on the feedback from reward model. During the iterative generation process, we introduce a momentum babsee process reward function that effectively balances exploration and exploitation. Experimental results demonstrate that Vinci significantly improves both image generation and cornuction-olllwiini abilities, achieving remarkable +22% performance improvement over the base model. This establishes Vinei as a new paradigm for reasoning-centric generative systems.
7 Acknowledgements
This work was supported by the "Pioneer" and "Leading Goose" R&D Program of Zhejiang (2025C02022) and the National Natural Science Foundation of China (No.62037001, No. No.62307032).

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Number
0
Roa5o Text Longth in MCDT Data
Figure 5: Distribution of reasoning text lengths in our MCoT dataset
A Dataset Construction
A.1 MCoT Construction Pipeline
To construct our Multinoda-CoT(MCCTT dataset, designed multi-stage pipeline comprising Image Generation for MCoT, Text Generation for MCoT, and MCoT Construction. Given text query q. first generated set of n candidate images ( ..., P2,.... Po. using FLUX.1-dev BS To identify objects, we applied Mask2Former 77 to each image, yielding detection outputs og and forming the set of tuples {P1. 01), (p2, 02), (Pn, 0n)), where og denoted the objects detected in image ps. Then, to mitigate ambiguity in multi-image interpretation. we tasked Qwen-Ve VL [68] with independently evaluating each tuple (p,, o;) for its semantic alignment with the original query and overall quality. The detection result was explicitly provided to the model to reduce hallucinations. During this evaluation, the model produced a caption e; and a quality score Jis resulting in annotated triples (pr, c. 9:). These were sorted in ascending order by to yield a ranked sequence of annotated samples. Finally, to construct the MCoT data, we randomly selected triples from the sorted list while preserving their relative score order. Each selected set must contain at least one high- quality (i.e., high-scoring) triple, which served as the final reasoning step. The resulting combination ((pt c. .. (.),,..,(())) (pf of .,(")) was input into Qwen-VIe VL to construct the final MCoT data. The prompts used by Qwen- during the construction process are shown in Table
The final MCoT dataset, consisting of 20,000 samples, comprises three types: 1-step MCoT, 2-step MCOT, and 3-step MCoT, which correspond reasoning processes involving 1, 2, and steps before generating the final image, respectively. Examples of these three types are shown in Table
A.2 Distribution of Reasoning Text Lengths
key characteristic of MCoT data the ability to clearly understand and articulate the content of images generated throughout the reasoning process. Therefore, the length of the accompanying text is an important aspect to consider. The distribution of reasoning text lengths in our constructed MCoT data is shown in Figure
B QA-based Reward for Generated Text
In our research, we designed a QA-based reward function to evaluate the quality of the generated text the context of text-to-image generation. This reward function assesses whether the generated text accurately describes the generated image, identifies any issues in the image, and provides strategies for improvement. Table 5 we provide examples of the prompts used to guide the evaluation process.
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MCoT Data Type
Example
1-step MCoT
<think>
The user asked to generate image: photo of two tennis rackets. One possible image was <1, _image>. The image cleurly reflected the prompt, showing two rackets placed side by side with distinct yet harmonious designs. The rackets were rendered in sharp detail, with red and black colors creating vivid contrast. A soft, neutral background kept focus on the objects. Lighting and composition gave the image a polished, professional look, making a faithful and visually appealing realization of the prompt. Overall, <1_ <i_amage> was an excellent realization of the prompt, and I selected it as answer because not only met the basic requirements but also elevated the concept through its high-quality execution and attention detail.
</think>
<anaver><1, 1aggar>>x_.naaa>>/aaserrrr
2-step MCoT
<think>
The user asked generate an image: a photo sandwich below sports hall. One possible image was <1 s__inage>. While <1_ "1_inage> did present a sandwich with sports ball above it, the sandwich appeared somewhat simplistic and lacked detailed texturing. I needed generate image that cleurly showed sandwich below sports hall, with the sandwich rendered in pleasing way.
realistic and aesthetically
Then, I generated <2, image>, which offered more refined and visually compelling representation of the prompt. The image clearly showed a sports ball above a sandwich, and their spatial relationship was accurate. The sandwich in <2 pm_imaee> had a more realistic texture, with visibly toasted bun that added depth and detail, The layers, including lettuce and other fillings, were vibrant and well-defined.
summary, <2_ image? correctly depicted a sandwich below a sports ball with a clear spatial ar- rangement. Both the sandwich and the sports ball featured improved textures and fine-grained details, contributing to the overall quality. These factors made <2_ image> the preferred chnice over <1_ miinngee.. </think>
<ansver><2 Banaver><__inage><4 inagg></anaaerrr
3-step MCoT
<think>
The user asked to generate an image: photo an orange backpack green spoon. One possible image <1_image>. However, did include an orange backpack as expected, which was critical element of the prompt. needed generate an image that included an orange backpack. then generated <2_inage>, which included the required orange backpack and aligned hetter with the prompt. While <2_image> was inprovement. its color scheme was relatively dull. and the background did not effectively complement the orunge backpack. I needed to make the colors more vibrant.
Finally. I generated <3_ image>, which featured a more vibrant and consistent color palette. The orange backpuck and green spoon stood out clearly against light green background. The lighting was evenly distributed, enhancing the textures and colors of the objects, which appeared more realistic and detailed. summary, selected <3, "_innge>> as the final result because it accurately captured both the orange hackpack and green spoon with </think>>
and visually pleasing composition.
<anaver><3 www.apg>>>aannaa><eeee> Sraage></anaverr"
Table 3: Introduction of the three types of MCoT data used in our cold-start stage. <1_image> p_image>,, and <3_ 3__mmage> represent images generated after 1, 2, and 3 rounds of reasoning, respectively. The corresponding visual tokens are omitted for brevity.
In our implementation, we used a multi-modas large language model (MLLM) to evaluate the generated text based on the prompts provided above. The scores and explanations provided by the MLLM were then used as part of the overall reward function to guide the training of our text-to-image generation model. By incorporating this QA-based reward function, we aimed to enhance the model's ability to generate high-quality, contextually accurate, and self-reflective image deseriptions, thereby improving the overall performance of the text-to-image generation process.
C
Implementation Details
C.1 Training Details
For our training, we adopted Emu3-Gen [7]] as the base model, which has unified image repre- sentation. The model's 's context length was extended to 15,360 tokens, which allowed for processing longer sequences of text and image pairs and supported approximately three iterations within each context window. The learning rate was set to le-5 and a beta of 0.01.
During the training, we utilized 16 A800 GPUs. The training was divided into two stages. In the first stage, known as the cold start, we set the batch size to 64 and trained for approximately 20 hours, allowing the model to learn the fundamental features and patterns of the data. In the second stage,
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Stage
Prompt Example
Text Generation
You are tasked with generating caption for given image and its detection results, and evaluating how well the image aliigns with the original prompt in terms of semantic sccuracy and generation quality.
Your task involves the following steps:
Caption Generation
Generate cohcrent and informative caption that accurately describes the given image. using the provided object detection results reference.
The caption should cover key visual elements, including object types, positions, colors, and spatial relationships, and reflect the intent of the original prompt.
Prompt-Imag Alignment and Generation Quality Evaluation
Evaluate how well the image itself matches the original prompt terms semantic content. addition, the visual quality of the generuted image, including realism, clarity, and overall aesthetic quality.
3. Scoring
Provide overull score on scale from to 4 bused on both prompt alignment and image quality: align prompt
indicates the image and aesthetically.
visual quality. fails both semantically
1 indicutes the image visually decent but fails cupture the semantics the prompt. It may contain hallucinated unrelated content.
2 indicates the image partially aligned with the prompt and has moderate quality. Some key elements
may be missing or inaccurately rendered.
3 indicates the image correctly reflects the prompt but suffers from low visual quality (e.g.. blurry.
distorted, or unnatural rendering).
4 indicates the image is fully aligned with the prompt and of high visual quality. It accurately presents
required elements in a realistic. clear, and aesthetically pleasing manner.
MCoT Construction
You are given sequence of (image, caption, score) triples ranked in ascending order by their score and their original prompt. where cach triple consists of: an image generated based on a text prompt,
a caption describing the image, a score indicuting how well the image aligns with the original prompt in terms semantic relevance and generation quality.
Your task is to simulate step-by-step reasoning process that leads to the final decision about which image best satisfies the original prompt. This process should reflect how human might evaluate and revise image generations based on feedhack and visual inspection.
Please proceed as follows:
1. Anulyze each image and cuption in order, reflecting what aspects are missing, incorrect, can be improved.
2. Describe how the reasoning evolves acress steps and why one image is better than the previous ones.
3. End the reasoning by selecting the hest image and briefly summarizing why it the final choice.
Output:
Your response must strictly follow the format below:
<think>
The user asked/requested to generate image: [prompt]. possible image <1_ image> [your generation and reasoning process]
</think>
<answer><yourr </onse)</answer>
Table 4: Introduction of prompts used in MCoT construction
which involved reinforcement learning, we configured the group size to 8 and trained for 60 hours, further optimizing the model's performance through external feedback mechanisms.
C.2 Data Preparation
All of our images are in the resolution of 512 512, and vision tokens generated using vision tokenizer of Emu3. Following the Emu3 design, we incorporated five special tokens to merge textual and visual data, constructing interleaved vision-language MCoT data and document-like inputs for the training process.
Taking the 2-step MCoT data as an example, whenever <1 <1_image> or <2 2__image> appears for the first time in the MCoT sequence, it is immediately followed by the corresponding image token block. The resulting training data can be structured follows:
<1 image> [BOI] (meta text) [SOT) {vision tokens) [EOL] [EOF] [EOI]
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Example
You are tasked with evaluating the quality of an image description generated by text-to-image model. Please provide a detailed evaluation based on the following aspects:
1. Completeness of the Image Description:
Assess whether the description covers all the key elements and details present the image. Consider whether it includes descriptions of objects, their positions, colors, and any other relevant visual attributes.
Identification of Issues the Image:
Determine if the description identifies any discrepancies issues in the generated image compared to the original prompt. This could include missing elements, incorrect colors, misplaced objects, or any other inconsistencies.
3. Strategics for Improvement:
Suggest specific strategies adjustments that could improve the accuracy and quality of the generated image. This could involve changes to the prompt or other recommendations to enhance the alignment between the generated image and original prompt.
Image Description: [Generated Image Description]
Please provide a score for each aspect scale of 0 to 2, where:
0 indicates poor performance,
indicates average performance,
2 indicates excellent performance. Additionally, provide a brief explanation for each score to justify your evaluation.
Table 5: Introduction of the prompt used to evaluate the generated text's quality.
Here, the image token block begins with [BOI], where (meta text) contains information about the image resolution. The token [SOT] marks the beginning of the vision token sequence. Additionally, [EOL] and [EOF] are inserted into the token stream indicate line breaks. The image token block ends with [EOI].
D Societal Impacts
Vinci, a novel framework that integrates deep reasoning capabilities into text-to-image generation through a unified model with reinforcement learning, has the potential to significantly enhance various applications that rely on visual content creation [42 43 54 66
69, 80) and understanding (20) 21
73 74] 76, 81. One of the key positive impacts is the improvement in the quality and alignment of generated images with textual prompts. This capability can greatly benefit educational and creative industries. For instance, in education, Vinci can generate highly accurate and contextually relevant illustrations for textbooks (27 48 70), making learning materials more engaging and accessible for students. In creative industries, such as graphic design and advertising, Vinci can assist designers in quickly generating high-quality visual concepts, thereby accelerating the creative process and potentially leading to more innovative and diverse visual content.
[4 40 44 46
47 67
Despite its potential benefits, Vinci also poses several risks that need be carefully considered. One of the primary concerns is the potential misuse of the technology for generating misleading or harmful visual content. Vinci's ability generate high-quality images based textual prompts increases the risk of creating deepfakes or manipulated images that could be used to spread disinformation. manipulate public opinion, or harm individuals' reputations. The ease with which these images can be generated and disseminated poses significant threat to socictal trust and information integrity.
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Reviews:
[{"review_id": "zHcaUyf5Ri", "reviewer": "Reviewer_4ED1", "summary": "Vinci is a framework for training a multimodal, reasoning style model for generating images through iterative refinement of the image in the reasoning process. The paper first generates a cold-start dataset of reasoning chains and then use reinforcement learning to optimize according to a number of reward functions based on object detection models, or other suite if image quality evaluators. The authors finally introduce a process-based reward function based on the Adam optimizer. Vinci demonstrates strong performance across a variety of tasks.", "questions": "- What is the significant of having R_i and then momentum reward function? Doesn't the moment reward function also encompass R_i? I find this was the interesting ablation that was missing.\n- Why does table 2 compare against the base model instead of the SFT'd model? It is unclear the benefit from SFT vs RL here.\n- What is the benefit of Vinci over, say, using a fixed image generation model and doing a similar method but to rewrite the prompt instead of training the image generator.\n- In the 3 generation scenario, do we actually see improvement across steps? I think this point is important to verify.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n- This paper is well written and easy to understand.\n- Aligning models in image generation to match prompt and user intent is a significant problem that Vinci addresses.\n- Vinci shows strong results on most tasks, performing well compared to leading closed source image generation models.\n\n**Weaknesses**\n- The CoT cold-start generation seems sub-optimal. Dataset collection has significant room for error after relying on the concatenation of so many models calls. Further, the authors have the model output a score from MLLM, which could be less grounded than head to head comparison, a common method for LLM as a judge comparison RLHF. Absent of the authors releasing the dataset, reproducibility may be difficult.\n- Scalability of the method is limited as reasoning chains for these models can become very long due to image tokens.", "was_revised": true, "final_justification": "The authors address my concerns and the other reviewers. Thus I think this paper deserves a borderline accept.\n\nMy reasoning for not deserving a higher score is that the techniques used are the same as those in LLMs, so there is not too much innovation in terms of transferrable techniques that can help other domains, which I expect for something with a higher score.", "rebuttal": "Thank you for your valuable feedback. We address each of your points individually below.\n\n\n\n**Q1. About the cold-start**\n\nWe disagree with the concern that cascading multiple models necessarily amplifies error. The staged pipeline is intentionally designed to constrain error, not propagate it. By decomposing data collection into sequentially verifiable sub-tasks, we can interleave human inspection at each checkpoint, yielding lower cumulative error than any single end-to-end model that lacks such intervention points.  Moreover, the cold-start phase is scoped to *format standardization*—i.e., ensuring variable-length, interleaved text–image outputs conform to a strict schema—rather than boosting raw generation quality. Improvements in generation fidelity are eserved for the subsequent reinforcement-learning phase, whose quantitative gains are reported in *Q4*.  Due to space constraints we cannot include external links in this rebuttal, but the full dataset and codebase will be released concurrently with the camera-ready version to enable reproducibility and future extensions.\n\n---\n\n**Q2. Scalability of the method**\n\nThanks to the rapid progress in the community, many multimodal large language model now support context windows of up to 128k tokens, enabling the generation of substantially longer multimodal sequences [1, 2]. Complementary efforts have simultaneously compressed visual representations—e.g., Anole reduces image tokens to only 1024. We argue, however, that the current bottleneck is not the length of the reasoning chain, but rather in how to achieve better reasoning during the image generation process.  \nThis observation motivates the core contribution of our work: a novel end-to-end framework for interleaved text–image chain-of-thought reasoning.\n\n\n>[1] Thinking with Generated Images\n\n>[2] SAM-R1: Leveraging SAM for Reward Feedback in Multimodal Segmentation via Reinforcement Learning\n\n---\n\n**Q3. About the $R_i$ and the momentum reward function**\n\nThank you for the valuable suggestion. $R_i$ is employed to assess the quality of a single generated image, whereas $R_m$—the momentum-based reward function—evaluates the quality of an entire image sequence by explicitly measuring whether the model makes consistent improvements across successive generation rounds. Following your recommendation, we have added a new ablation study (see the table below).\n\n| Model                            | Single Obj | Two Obj | Counting | Colors | Position | Color Attr. | Overall |\n|----------------------------------|------------|---------|----------|--------|----------|--------------|---------|\n| Emu3-Gen                         | 0.98       | 0.71    | 0.34     | 0.81   | 0.17     | 0.21         | 0.54    |\n| w. $R_t$                         | 0.98       | 0.76    | 0.43     | 0.81   | 0.53     | 0.28         | 0.63    |\n| w. $R_i$                         | 0.96       | 0.73    | 0.36     | 0.75   | 0.72     | 0.44         | 0.66    |\n| w. $R_m$                         | 0.97       | 0.74    | 0.32     | 0.84   | 0.31     | 0.24         | 0.57    |\n| w. $R_i$ & $R_t$                 | 0.99       | 0.84    | 0.44     | 0.79   | 0.75     | 0.46         | 0.71    |\n| w. $R_i$ & $R_m$                 | 0.99       | 0.75    | 0.39     | 0.75   | 0.76     | 0.46         | 0.68    |\n| w. $R_t$ & $R_m$                 | 0.99       | 0.77    | 0.44     | 0.82   | 0.59     | 0.33         | 0.66    |\n| w. $R_i$ & $R_t$ & $R_m$         | 0.99       | 0.86    | 0.48     | 0.83   | 0.86     | 0.54         | 0.76    |\n\nThe quantitative results of this ablation, together with those reported in **Q6**, confirm that $R_m$ enables the model to perform effective self-correction toward higher-quality outputs.\n\n---\n\n**Q4. The results of SFT's model**\n\nThank you for your suggestion. We have incorporated the results of SFT's model as shown in the table below.\n\n| Model              | Single Obj | Two Obj | Counting | Colors | Position | Color Attr. | Overall |\n|--------------------|------------|---------|----------|--------|----------|--------------|---------|\n| Emu3-Gen           | 0.98       | 0.71    | 0.34     | 0.81   | 0.17     | 0.21         | 0.54    |\n| Emu3-Gen (SFT)     | 0.98       | 0.74    | 0.39     | 0.81   | 0.50     | 0.24         | 0.61    |\n| Vinci              | 0.99       | 0.86    | 0.48     | 0.83   | 0.86     | 0.54         | 0.76    |\n\nThank you for the insightful suggestion. We have accordingly supplemented Supervised Fine-Tuning (SFT) results, presented in the table below. While SFT yields a modest improvement (+7), the dominant gain (+15) emerges from the reinforcement-learning phase. This outcome substantiates that RL is significantly more effective than SFT in transferring the model’s visual-understanding capabilities into the image-generation process.\n\n---\n\n**Q5. Compare with the rewrite-based methods**\n\nWe appreciate the suggestion and have now included a comparative experiment against prompt-rewriting baselines; the results, shown in the table below, demonstrate that our approach consistently outperforms existing rewriting methods. \n| Model         | Single Obj | Two Obj | Counting | Colors | Position | Color Attr. | Overall |\n|---------------|------------|---------|----------|--------|----------|--------------|---------|\n| Emu3+rewrite   | 0.99       | 0.81    | 0.42     | 0.80   | 0.49     | 0.45         | 0.66    |\n| GOT [1]       | 0.99       | 0.69    | 0.67     | 0.85   | 0.34     | 0.27         | 0.64    |\n| T2i-R1 [2]    | 0.99       | 0.72    | 0.60     | 0.87   | 0.51     | 0.35         | 0.67    |\n| Vinci         | 0.99       | 0.86    | 0.48     | 0.83   | 0.86     | 0.54         | 0.76    |\n\nMoreover, our interleaved text–image chain-of-thought framework **eliminates the need for an extra rewrite model by leveraging self-reflection**, thereby reducing both computational cost and latency while delivering a superior user experience.\n\n>[1] GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing\n\n>[2] T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT\n\n---\n\n**Q6. The improvement across steps**\n\nDue to space constraints in the rebuttal, we are unable to provide additional visualizations (they will be added in the subsequent version). Instead, we report aggregated statistics over three-round scenarios in the table below.\n\n| Type | \\<image_0> | \\<image_1> | \\<image_2> | w $R_m$ (%) (232/553) | w/o $R_m$ (%) (222/553) |\n|------|-----------|-----------|-----------|------------------------|--------------------------|\n| 1    | ×         | ×         | ×         | 2.16                   | 4.50                     |\n| **2**    | **×**         | **×**         | **√**         | **28.02**                  | **18.92**                    |\n| 3    | ×         | √         | ×         | 1.72                   | 6.76                     |\n| 4    | √         | ×         | ×         | 4.74                   | 8.56                     |\n| 5    | √         | √         | ×         | 4.31                   | 7.66                     |\n| 6    | √         | ×         | √         | 3.88                   | 5.41                     |\n| **7**    | **×**         | **√**         | **√**         | **51.72**                  | **45.05**                    |\n| 8    | √         | √         | √         | 3.45                   | 3.15                     |\n\nResults show that the vast majority of triple-round generations exhibit continuous improvement **(Type 2 and 7 account for ≈ 80%)**. Furthermore, the same statistics after ablating $R_m$ drop to ≈ 63% for Type 2 and 7, which once again confirms that $R_m$ is instrumental in driving the model to iteratively refine its outputs.\n\nMoreover, we will add the reward curve in the subsequent version to demonstrate the stable improvement."}, {"review_id": "U9wpaAc21R", "reviewer": "Reviewer_QUHK", "summary": "This paper proposes to apply Multimodal Chain-of-Thought (MCoT) reasoning and Reinforcement Learning (RL) to text-to-image generation within a unified multimodal model, specifically Emu3. It follows the recent RL techniques in large-language models (LLMs), especially the Deepseek-R1. Specifically, it first collects a small amount of multimodal chain-of-thought data from GPT-4o, used for cold-start of the RL training. Then it introduces a momentum-based reward function that distributes process rewards based on historical improvements, which improves the long-term stability of RL optimization. This method is based on Emu3 because of its unified image representation. Experiments on GenEval have demonstrated +22% improvement compared to the base model.", "questions": "In addition to the Weaknesses, I have additional questions about the paper details.\n\n1. This paper does not compare with other CoT & RL image generation models such as T2I-R1. While they may be different in the overall architecture, they address a similar problem. A direct comparison, possibly by applying T2I-R1 to Emu3 under similar settings, would strengthen the results.\n\n2. Are there any methods that can be used to analyze or visualize the effectiveness of the momentum-based reward function and show that it stabilizes the training process?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1. This method explores applying Multimodal Chain-of-Thought (MCoT) and RL to cross-modal multimodal models, enabling iterative refinement over both image and text modalities. This is a step beyond prior unimodal CoT approaches, which focus only on textual prompt refinement.\n\n2. It proposes a momentum-based reward function, which is a meaningful contribution that addresses the common instability issues in RL finetuning.\n\n3. The results on GenEval have shown good improvement compared to Emu3.\n\nWeaknesses\n\n1. The method design depends on a unified autoregressive model that has unified text and image representations, which limits its application to different MLLMs.\n\n2. The MCoT data is only collected by GPT-4o without real-world data, which will possibly be limited by GPT-4o's capabilities.\n\n3. The evaluation is limited to GenEval, which contains short prompts and may not be enough to evaluate the CoT capabilities. Experiments on other benchmarks such as DPG-Bench from ELLA and T2I-CompBench should be included.\n\nReference: ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment\n\n4. The ablation study only evaluates the effectiveness of reward functions. However, the alterations with/without the MCoT data should also be included to validate the effectiveness of the cold start phase.", "was_revised": true, "final_justification": "Most of my concerns are addressed in the rebuttal. I raised the score towards accept.", "rebuttal": "Thank you for your valuable feedback. We address each of your points individually below.\n\n\n**Q1. The limitations of the application**\n\nWe contend that this should not be regarded as a limitation of our work. Recently, the community has devoted considerable attention to unified autoregressive frameworks [1, 2, 3] that represent text and images within a single modality; consequently, a growing family of such unified models has emerged. We align with the position articulated in [1] that *a truly unified autoregressive model should adopt a unified representation for both text and images*—without which it cannot claim to be a foundational unified system. Through this paper, we also hope to further encourage the community to reflect on and refine the architectural design of unified autoregressive models.\n\n> [1] Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning\n\n> [2] Chameleon: Mixed-Modal Early-Fusion Foundation Models\n\n> [3] X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again\n\n\n---\n\n**Q2. Limited by GPT-4o's capabilities**\n\nWe did not use GPT-4o to produce the MCoT annotations. High-quality human labels are prohibitively expensive to collect at scale; therefore, the current MCoT dataset pairs images generated by Flux with captions provided by Qwen-VL. GPT-4o is used to evaluate and filter out data that contains hallucinations or failures at the MCoT endpoints.  \nEmpirically, sampling indicates that both Qwen-VL and GPT-4o exhibit strong and robust comprehension of the generated images, and we have not observed any systematic failures or notable shortcomings. The complete dataset will be released concurrently—please stay tuned.\n\n---\n\n**Q3. The results based on other benchmarks**\n\nFollowing your suggestion, we have added results on **DPG-Bench** and **T2I-CompBench**. Vinci ranks second on DPG-Bench—only marginally behind Janus-Pro-7B—and achieves the highest scores on T2I-CompBench, demonstrating a substantial improvement. These additional benchmarks further corroborate the effectiveness of Vinci.\n\n**Table 1. DPG-Bench**\n\n| Method        | Overall | Global | Entity | Attribute | Relation | Other  |\n|---------------|---------|--------|--------|-----------|----------|--------|\n| **Gen. Only** |\n| SD3-Medium    | 84.08   | 87.90  | 91.01  | 88.83     | 80.70    | 88.68  |\n| Flux.1 dev    | 84.00   | 82.10  | 89.50  | 88.70     | 91.10    | 89.40  |\n| **Unified.**  |\n| Show-o        | 67.27   | 79.33  | 75.44  | 78.02     | 84.45    | 60.80  |\n| Janus-Pro-7B  | 84.19   | 86.90  | 88.90  | 89.40     | 89.32    | 89.48  |\n| Emu3          | 80.60   | 85.21  | 86.68  | 86.84     | 90.22    | 83.15  |\n| Vinci-sft     | 81.18   | 86.12  | 86.91  | 87.53     | 90.64    | 82.98  |\n| Vinci         | 82.54   | 87.68  | 88.03  | 87.72     | 91.02    | 85.43  |\n\n**Table 2. T2I-CompBench**\n\n| Model         | Color   | Shape  | Texture | Spatial | Non-Spatial | Complex |\n|---------------|---------|--------|---------|---------|-------------|---------|\n| **Gen. Only** |\n| SD3-Medium    | 0.8132  | 0.5885 | 0.7334  | 0.3200  | 0.4084      | 0.3771  |\n| Flux.1 dev    | 0.7407  | 0.5718 | 0.6922  | 0.2863  | 0.3127      | 0.3703  |\n| **Unified.**  |\n| Show-o        | 0.5600  | 0.4100 | 0.4600  | 0.2000  | 0.3000      | 0.2900  |\n| Janus-Pro-7B  | 0.6359  | 0.3528 | 0.4936  | 0.2061  | 0.3085      | 0.3559  |\n| T2I-R1        | 0.8130  | 0.5852 | 0.7243  | 0.3378  | 0.3090      | 0.3993  |\n| Vinci-sft     | 0.6274  | 0.4729 | 0.6189  | 0.2292  | 0.2711      | 0.3752  |\n| Vinci         | 0.8225  | 0.6062 | 0.7374  | 0.3243  | 0.3148      | 0.4081  |\n\n\n---\n\n**Q4. Ablation on MCoT**\n\nThank you for the suggestion. We have conducted an ablation on the MCoT data:\n\n- **w. MCoT**: SFT with MCoT followed by RL.  \n- **w/o MCoT & w. RL**: Skip SFT and train directly with RL, using only a format reward to learn the interleaved output pattern.\n\nResults show that the primary benefit of MCoT during cold-start is to support the interleaved output format; it yields only a modest improvement in generation quality (+7) compared with the larger gains delivered by RL (+15).  \nFor Emu3—whose native architecture lacks interleaved text–image capability—learning the format solely through the format reward is difficult, leading to a significant drop in final performance (overall score: 64).\n\n **Table 3. MCoT Ablation Results**\n\n| Model                  | Single Obj | Two Obj | Counting | Colors | Position | Color Attr. | Overall |\n|------------------------|------------|---------|----------|--------|----------|--------------|---------|\n| Emu3-Gen               | 0.98       | 0.71    | 0.34     | 0.81   | 0.17     | 0.21         | 0.54    |\n| w. MCoT                | 0.98       | 0.74    | 0.39     | 0.81   | 0.50     | 0.24         | 0.61    |\n| w/o MCoT & w. RL       | 0.99       | 0.74    | 0.42     | 0.82   | 0.57     | 0.29         | 0.64    |\n| w. MCoT & RL           | 0.99       | 0.86    | 0.48     | 0.83   | 0.86     | 0.54         | 0.76    |\n\n---\n\n**Q5. Compare with T2I-R1**\n\nWe further benchmarked Vinci against T2I-R1 on both **Geneval** and **T2I-CompBench**. Vinci substantially outperforms T2I-R1 across all metrics. Notably, T2I-R1 is built upon Janus-Pro-7B—a stronger backbone than Emu3—underscoring the effectiveness of our approach.\n\n**Table 4. Geneval**\n\n| Model   | Single Obj | Two Obj | Counting | Colors | Position | Color Attr. | Overall |\n|---------|------------|---------|----------|--------|----------|--------------|---------|\n| T2I-R1  | 0.99       | 0.77    | 0.70     | 0.87   | 0.41     | 0.30         | 0.67    |\n| Vinci   | 0.99       | 0.86    | 0.48     | 0.83   | 0.86     | 0.54         | 0.76    |\n\n**Table 5. T2I-CompBench**\n\n| Model   | Color  | Shape  | Texture | Spatial | Non-Spatial | Complex |\n|---------|--------|--------|---------|---------|--------------|---------|\n| T2I-R1  | 0.8130 | 0.5852 | 0.7243  | 0.3378  | 0.3090       | 0.3993  |\n| Vinci   | 0.8225 | 0.6062 | 0.7374  | 0.3243  | 0.3148       | 0.4081  |\n\n---\n\n**Q6. The effectiveness of the momentum-based reward function**\n\nDue to space constraints in the rebuttal, we are unable to provide additional visualizations (they will be added in the subsequent version). Instead, we report aggregated statistics over three-round scenarios in the table below.\n\n\n| Type | \\<image_0> | \\<image_1> | \\<image_2> | w $R_m$ (%) (232/553) | w/o $R_m$ (%) (222/553) |\n|------|-----------|-----------|-----------|------------------------|--------------------------|\n| 1    | ×         | ×         | ×         | 2.16                   | 4.50                     |\n| **2**    | **×**         | **×**         | **√**         | **28.02**                  | **18.92**                    |\n| 3    | ×         | √         | ×         | 1.72                   | 6.76                     |\n| 4    | √         | ×         | ×         | 4.74                   | 8.56                     |\n| 5    | √         | √         | ×         | 4.31                   | 7.66                     |\n| 6    | √         | ×         | √         | 3.88                   | 5.41                     |\n| **7**    | **×**         | **√**         | **√**         | **51.72**                  | **45.05**                    |\n| 8    | √         | √         | √         | 3.45                   | 3.15                     |\n\nResults show that the vast majority of triple-round generations exhibit continuous improvement **(Types 2 and 7 account for ≈ 80%)**. Furthermore, the same statistics after ablating $R_m$ drop to ≈ 63% for Types 2 and 7, which once again confirms that $R_m$ is instrumental in driving the model to iteratively refine its outputs.\n\nMoreover, we will add the reward curve in the subsequent version to demonstrate the stable improvement."}, {"review_id": "HXBmBRSb8R", "reviewer": "Reviewer_5wxe", "summary": "The authors introduce Vinci, a novel framework that integrates multimodal Chain-of-Thought (MCoT) reasoning with reinforcement learning (RL) to enhance text-to-image generation. Unlike existing unified models that treat image generation and understanding as separate tasks, Vinci enables iterative refinement of generated images through real-time self-reflection.", "questions": "1) Why exclude comparisons to RL-based T2I models like DALL-E 3? Including these would strengthen claims of superiority.\n\n2) Provide specifics on the object detection model and GPT-4o filtering thresholds for MCoT data construction.\n\n3) Derive or cite theoretical guarantees for Equation 5’s stability.\n\n4) Quantify the computational overhead of iterative refinement, e.g., FLOPs per iteration.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1) The integration of MCoT with RL for iterative image refinement is novel. The momentum-based reward function is a creative solution to stabilize multi-round generation.\n2) Comprehensive experiments on GenEval show significant gains (+22% over baselines), with detailed ablation studies validating each component’s contribution.\n​​3)  The framework addresses a critical gap in unified models by enabling real-time self-correction, as shown in qualitative results.\n\nWeaknesses\n1) While the authors compare with unified models (e.g., Janus-Pro, GPT-4o), it omits comparisons to recent RL-based T2I models like DALL-E 3 or Stable Diffusion 3, which are industry standards.\n​​2) The MCoT data pipeline lacks details on object detection models and GPT-4o filtering criteria. Hyperparameters for RL training are not justified.\n3) The momentum reward’s theoretical guarantees (e.g., convergence) are not discussed, despite its central role.", "was_revised": true, "final_justification": "The authors addressed all my concerns from Weaknesses and Questions, so I lifted my final score.", "rebuttal": "Thank you for your valuable feedback. We address each of your points individually below.\n\n**Q1. Compare with RL-based T2I models**\n\nWe have added comparisons with recent RL-based text-to-image models (Tables 2 and 3 in [1]); the results are presented below. **Vinci** outperforms both RL-fine-tuned Stable Diffusion and FLUX, primarily because our pipeline incorporates a *self-reflective mechanism* during reinforcement learning that leverages the model’s own visual-understanding capabilities to enhance generation quality.\n\n| Model                                              | Geneval Score |\n|----------------------------------------------------|----------------|\n| Stable Diffusion with HPS-v2.1 & CLIP Score        | 0.52           |\n| FLUX with HPS-v2.1 & CLIP Score                    | 0.69           |\n| **Vinci**                                          | **0.76**       |\n\n---\n\n**Q2. More details about MCoT data construction**\n\nFor the object detection model, we use Mask2Former with a Swin-S backbone, following the official MMDetection configuration and pretrained on the COCO dataset. We apply a detection confidence threshold of 0.6 to remove low-confidence predictions. For computational efficiency and consistency, we retain at most 16 objects per class per image.\n\nDuring data filtering, we introduce GPT-4o as a final quality filter to identify hallucinations or incoherent reasoning chains. Specifically, for each constructed MCoT sample, we query GPT-4o with a structured prompt to evaluate:\n\n1. Whether the final image is semantically aligned with the original prompt;  \n2. Whether the reasoning steps are logically coherent and progressively improve the image’s consistency or visual quality.\n\nGPT-4o provides a binary judgment (\"pass\"/\"fail\") along with a short explanation. We retain only the samples that pass this check and discard the rest. In practice, this filtering process removes approximately 8% of low-quality MCoT samples.\n\n---\n\n\n**Q3. Derive theoretical guarantees for Equation 5’s stability**\n\nBelow, we provide a comprehensive analysis of the reward function’s theoretical underpinnings.\n\n\n### 1. Convergence Analysis\n\n**Finite Sequence Length ($n$):**  \nFor a finite sequence length $n$, all terms in the process reward function $R_m$ are finite sums, which guarantees that the value of $R_m$ remains finite and computable:\n$$\nR_m \\text{ is bounded and computable for finite } n.\n$$\n\n**As $n \\to \\infty$:**\n\na. The first term, which averages the quality scores $R_i(s_t)$, converges to the expected quality score, assuming a stable sequence:\n$$\n\\lim_{n \\to \\infty} \\frac{1}{n} \\sum_{t=1}^{n} R_i(s_t) = \\mathbb{E}[R_i(s_t)].\n$$\n\nb. The second term, which accounts for momentum-based improvement, converges depending on the decay factor $\\lambda$:\n\n\\begin{cases}\n\\lambda < 1: & \\sum_{k=1}^{\\infty} \\frac{\\Delta_k \\lambda^{k-1}}{\\sqrt{V_k} + \\epsilon} \\leq \\frac{1}{\\epsilon(1 - \\lambda)} \\quad \\text{(absolutely convergent)}\n\\end{cases}\n\n\\begin{cases}\n\\lambda = 1: & \\sum_{k=1}^{n-1} \\frac{\\Delta_k}{\\sqrt{V_k} + \\epsilon} \\leq \\frac{n - 1}{\\epsilon} \\quad \\text{(bounded for finite $n$)}\n\\end{cases}\n\n\nThus, the reward function $R_m$ guarantees convergence as $n \\to \\infty$, with the sum of improvements tending to a stable value based on the decay rate $\\lambda$.\n\n---\n\n### 2. Edge Case Testing\n\nThe behavior of the reward function in edge cases demonstrates its adaptive nature and stability:\n\n- **No Improvement** ($\\forall k, \\Delta_k = 0$):\n  $$\n  R_m = \\frac{1}{n} \\sum_{t=1}^{n} R_i(s_t) \\quad \\text{(degenerates to the average quality score)}\n  $$\n\n- **Initial Large Improvement** ($R_i(s_0) = 0$, $R_i(s_1) = 1$):\n  $$\n  V_1 = 0.1, \\quad \\frac{\\Delta_1}{\\sqrt{V_1} + \\epsilon} \\approx 3.162 \\quad \\text{(modest amplification of early improvement)}\n  $$\n\n- **Low Variability Small Improvements** ($\\Delta_k = 0.01$, $V_k = 10^{-6}$):\n  $$\n  \\frac{\\Delta_k}{\\sqrt{V_k} + \\epsilon} \\approx 10 \\quad \\text{(adaptive amplification of consistent small gains)}\n  $$\n\n- **Constant Scores** ($\\forall t,\\ R_i(s_t) = c$):\n\n  $$\n  \\Delta_k = 0 \\Rightarrow R_m = c \\quad \\text{(no momentum contribution)}\n  $$\n\nWhen the quality scores are constant, the reward function focuses entirely on the absolute quality score, with no momentumcomponent.\n\n---\n\n### 3. Mathematical Stability Conclusions\n\nWe formally summarize the stability guarantees of the momentum reward function $R_m$:\n\n- **Definition Completeness:**  \n  $$\n  \\sqrt{V_k} + \\epsilon > \\epsilon > 0 \\quad \\text{ensures no division by zero}.\n  $$\n\n- **Global Boundedness:**  \n  $$\n  R_m \\in \\left[0,\\ 1 + \\alpha \\cdot \\min\\left( \\frac{1}{\\epsilon(1 - \\lambda)},\\ \\frac{n - 1}{\\epsilon} \\right) \\right],\n  $$\n  ensuring $R_m$ remains within a finite and interpretable range.\n\n- **Numerical Robustness:**  \n  Setting $\\epsilon = 10^{-8}$ avoids underflow and overflow. All inputs are bounded:\n  $$\n  R_i(s_t) \\in [0, 1], \\quad \\Delta_k \\in [0, 1], \\quad V_k \\geq 0.\n  $$\n\n- **Convergence Guarantee:**  \n  For $\\lambda < 1$, the infinite sum of improvements is absolutely convergent:\n  $$\n  \\lambda < 1 \\Rightarrow \\text{long-term stability and consistent reward computation}.\n  $$\n\nIn conclusion, the momentum process reward function $R_m$ exhibits robust theoretical properties that ensure its stability and convergence during training.\n\n\n---\n\n**Q4. The FLOPs per iteration**\n\nWe measured the computational cost across different iterations; the results are shown in the table below. The overhead scales almost linearly with the number of generated images.\n\n| Model      | Inference Type | Image Samples | TFLOPs | TFLOPs (%) |\n|------------|----------------|----------------|--------|------------|\n| Emu3-Gen   | Direct T2I     | 1              | 25.3   | 100.00     |\n| Vinci      | MCoT           | 1              | 28.5   | 112.65     |\n|        Vinci    |          MCoT       | 2              | 56.9   | 224.11     |\n|     Vinci       |     MCoT            | 3              | 85.4   | 337.55     |"}, {"review_id": "Ic7Z7wKt8M", "reviewer": "Reviewer_2Qyn", "summary": "The paper introduces Vinci, a unified autoregressive text-to-image model that interleaves image generation and understanding through a Multimodal Chain-of-Thought (MCoT). After a small “cold-start” finetuning with 20 k synthetic MCoT traces, the authors apply reinforcement learning guided by three rewards—program-based visual checks, QA-based textual assessment, and a novel momentum reward—to let the model iteratively critique and refine its own outputs. On the object-focused GenEval benchmark, Vinci improves the Emu3-Gen base by +22 % overall, narrowing the gap with much larger systems while remaining fully end-to-end.", "questions": "What is the training cost and inference cost of the model compared with direct or simple cot?", "limitations": "See weaknesses", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n1. Conceptual novelty – The work realizes interleaved MCoT inside a single autoregressive generator, enabling the model to “look at” intermediate images and revise them on-the-fly. This directly tackles the reflection bottleneck of previous unified methods.\n\n2. Reward design – The momentum-based reward elegantly couples short-term quality with long-term improvement stability, an idea reminiscent of adaptive optimisers yet new to RLHF for vision–language models.\n\n3. Thorough empirical evidence – Results span benchmark scores, qualitative rollouts and an ablation that disentangles each reward’s contribution, convincingly supporting the claimed +22 % gain and showing Vinci to be competitive with much larger models.\n\nWeaknesses\n1. Limited evaluation scope – All quantitative claims rest on GenEval; there is no open-domain or human preference study, leaving generalisation and perceptual quality uncertain.\n\n2. Heavy reliance on automatically-synthesised MCoT traces. The “cold-start” stage uses just 20 k traces whose images come from Flux and captions from Qwen-VL. Because neither source is curated or human-checked, the traces may carry style biases or captioning errors that the policy then imitates, limiting robustness outside the synthetic data distribution.\n\n3. Both the program-based visual check and the QA-based textual check depend on external detectors or MLLMs. Any failure—e.g., detector mis-counting objects in cluttered scenes—directly contaminates the reinforcement signal, encouraging reward hacking rather than genuine image-text alignment.\n\n4. While the paper defines α, λ, γ for the momentum term, it never reports the chosen values or provides a sensitivity study.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback. We address each of your points individually below.\n\n\n\n**Q1. Expand the evaluation scope**\n\nWe thank the reviewers for this suggestion. Owing to the tight rebuttal schedule, we engaged three undergraduate annotators to conduct a human evaluation on **Geneval**; the results are summarized in the table below. Despite the inevitable noise in automatic metrics, the human judgements confirm that **Vinci** still outperforms all baselines.  \n\nAdditionally, we have now reported results on **DPG-Bench** and **T2I-CompBench**: Vinci ranks second on DPG-Bench—only marginally behind Janus-Pro-7B—and achieves the highest scores on T2I-CompBench, manifesting a substantial gain over Emu3. Taken together, these supplementary benchmarks further corroborate Vinci’s effectiveness.\n\n**Table 1. Human Evaluation**\n\n| Model      | Single Obj | Two Obj | Counting | Colors | Position | Color Attr. | Overall |\n|------------|------------|---------|----------|--------|----------|--------------|---------|\n|*Model-Based Evaluation*\n| Show-o     | 0.98       | 0.80    | 0.66     | 0.84   | 0.31     | 0.50         | 0.68    |\n| Emu3-Gen   | 0.98       | 0.71    | 0.34     | 0.81   | 0.17     | 0.21         | 0.54    |\n| T2I-R1     | 0.99       | 0.77    | 0.70     | 0.87   | 0.41     | 0.30         | 0.67    |\n| Vinci      | 0.99       | 0.86    | 0.48     | 0.83   | 0.86     | 0.54         | 0.76    |\n|*Human Evaluation*\n| Show-o     | 0.97       | 0.81    | 0.64     | 0.83   | 0.29     | 0.49         | 0.67    |\n| Emu3-Gen   | 0.98       | 0.69    | 0.35     | 0.81   | 0.18     | 0.18         | 0.53    |\n| T2I-R1     | 0.98       | 0.75    | 0.72     | 0.86   | 0.37     | 0.28         | 0.66    |\n| Vinci      | 0.99       | 0.82    | 0.47     | 0.78   | 0.88     | 0.48         | 0.74    |\n\n\n\n**Table 2.  DPG-Bench**\n\n| Model         | Overall | Global | Entity | Attribute | Relation | Other  |\n|---------------|---------|--------|--------|-----------|----------|--------|\n| **Gen. Only** |\n| SD3-Medium    | 84.08   | 87.90  | 91.01  | 88.83     | 80.70    | 88.68  |\n| Flux.1 dev    | 84.00   | 82.10  | 89.50  | 88.70     | 91.10    | 89.40  |\n| **Unified.**  |\n| Show-o        | 67.27   | 79.33  | 75.44  | 78.02     | 84.45    | 60.80  |\n| Janus-Pro-7B  | 84.19   | 86.90  | 88.90  | 89.40     | 89.32    | 89.48  |\n| Emu3          | 80.60   | 85.21  | 86.68  | 86.84     | 90.22    | 83.15  |\n| Vinci-sft     | 81.18   | 86.12  | 86.91  | 87.53     | 90.64    | 82.98  |\n| Vinci         | 82.54   | 87.68  | 88.03  | 87.72     | 91.02    | 85.43  |\n\n**Table 3. T2I-CompBench**\n\n| Model         | Color   | Shape  | Texture | Spatial | Non-Spatial | Complex |\n|---------------|---------|--------|---------|---------|-------------|---------|\n| **Gen. Only** |\n| SD3-Medium    | 0.8132  | 0.5885 | 0.7334  | 0.3200  | 0.4084      | 0.3771  |\n| Flux.1 dev    | 0.7407  | 0.5718 | 0.6922  | 0.2863  | 0.3127      | 0.3703  |\n| **Unified.**  |\n| Show-o        | 0.5600  | 0.4100 | 0.4600  | 0.2000  | 0.3000      | 0.2900  |\n| Janus-Pro-7B  | 0.6359  | 0.3528 | 0.4936  | 0.2061  | 0.3085      | 0.3559  |\n| T2I-R1        | 0.8130  | 0.5852 | 0.7243  | 0.3378  | 0.3090      | 0.3993  |\n| Vinci-sft     | 0.6274  | 0.4729 | 0.6189  | 0.2292  | 0.2711      | 0.3752  |\n| Vinci         | 0.8225  | 0.6062 | 0.7374  | 0.3243  | 0.3148      | 0.4081  |\n\n---\n\n**Q2. About the MCoT data**\n\nWe decompose data collection into sequentially verifiable sub-tasks. So we can interleave human inspection at each stage, yielding lower cumulative error than any single end-to-end model that lacks such intervention points.\n\nAlthough we did not conduct a thorough manual inspection, we used GPT-4 to evaluate and filter out data that contains hallucinations or failures at the MCoT endpoints.  \nEmpirically, sampling indicates that both Qwen-VL and GPT-4o exhibit strong and robust comprehension of the generated images, and we have not observed any systematic failures or notable shortcomings.\n\nDue to space constraints we cannot include external links in this rebuttal, but the full dataset and codebase will be released concurrently with the camera-ready version to enable reproducibility and future extensions.\n\n---\n\n**Q3. About the reward model**\n\nWe acknowledge that detector failures can corrupt the reinforcement signal; however, the human evaluation reported in Q1 shows that such instances are not too much.  \n\nDeveloping more robust reward models is undeniably important, yet it lies beyond the scope of this paper and is therefore deferred to future research.\n\n---\n\n**Q4. The hyperparameters for momentum reward**\n\nIn our actual implementation, we have $\\alpha = 0.5$, $\\lambda = 0.5$, and $\\gamma = 0.5$.  \nHere:\n- $\\alpha$ balances the single image quality score and momentum update;\n- $\\lambda$ is the time decay factor, encouraging improvement at earlier steps;\n- $\\gamma$ controls the sensitivity of $V_k$ to recent changes (smaller = more sensitive).\n\nDue to time constraints, we additionally conducted two sets of sensitivity studies, shown below:\n\n**Table 4. Hyperparameters Sensitivity Studies**\n\n| Setting                 | Single Obj | Two Obj | Counting | Colors | Position | Color Attr. | Overall |\n|------------------------|------------|---------|----------|--------|----------|--------------|---------|\n| $\\alpha=\\lambda=\\gamma=0.1$ | 0.99       | 0.83    | 0.51     | 0.80   | 0.84     | 0.55         | 0.75    |\n| $\\alpha=\\lambda=\\gamma=0.8$ | 0.99       | 0.85    | 0.50     | 0.82   | 0.85     | 0.55         | 0.76    |\n| $\\alpha=\\lambda=\\gamma=0.5$ (Ours) | 0.99       | 0.86    | 0.48     | 0.83   | 0.86     | 0.54         | 0.76    |\n\nThe results indicate that these hyperparameters have relatively little impact on the outcome.\n\n---\n\n**Q5. The training cost and inference cost**\n\n- **Cold-start stage**: batch size = 64, learning rate = 1e-5, weight decay $\\beta = 0.01$  \n  Duration: ~20 hours, to acquire basic multimodal generation capability.\n\n- **Reinforcement learning stage**: group size = 8, training time = 60 hours  \n  Purpose: refine performance via reward-guided optimization.\n\n**Table 5. Inference Cost**\n| Model      | Inference Type | Image Samples | Time (min) |    Memory (GB)  | Mem. (%) | Time (%) |\n|------------|----------------|----------------|-------------|------------|----------|----------|\n| Emu3-Gen   | Direct T2I     | 1              | 5.00        | 23         | 100.00   | 100.00   |\n| Vinci      | MCoT           | 1              | 5.87        | 24         | 104.35   | 117.33   |\n| Vinci      | MCoT           | 2              | 6.27        | 27         | 117.39   | 125.33   |\n| Vinci      | MCoT           | 3              | 6.80        | 28         | 121.74   | 136.00   |"}, {"review_id": "9pOytRS2iD", "reviewer": "Reviewer_CRiu", "summary": "The paper introduces a method to enhance the text-to-image generation capabilities of multimodal models that utilize discrete tokens with causal attention for both image and text generation, such as Emu3-Gen. Specifically, the authors incorporate multimodal chain-of-thought (MCoT) reasoning and self-reflection capabilities, enabling the model to progressively refine image generation during inference. Initially, the model undergoes supervised fine-tuning on synthetically generated MCoT data, followed by reinforcement learning guided by three reward functions: a program-based reward leveraging detection models for image evaluation, a question-answering reward using multimodal large language models (MLLM) to assess generated textual reasoning, and a momentum process reward that dynamically encourages incremental improvements based on the improvements across images in the MCoT.\n\nThe authors demonstrate the effectiveness of their method by achieving a significant improvement of +22 absolute points (from 54% to 76%) on the GenEval benchmark. The key contributions include the focus of MCoT and self-reflection into text-to-image generation, developing a pipeline for synthesizing MCoT training data, designing reward functions for reinforcement learning in text-to-image, and providing ablation studies to quantify the impact of each reward component.", "questions": "Questions and Suggestions for the Authors:\n\n1. Could you provide detailed performance metrics of the model immediately after the supervised fine-tuning (SFT) phase, prior to applying reinforcement learning (RL)? Including this information would significantly clarify the contribution of the RL phase.\n\n2. How does your MCoT-based method compare to a simpler baseline strategy that involves selecting the best image from multiple generations based solely on the image-based reward models? The baseline should be tuned on high quality generations that were collected and used during the cold-start phase to level the playing field of collecting generations using flux and the scoring and filtering them. Including this information can clarify the benefits of using the MCoT framework.\n\n3. Can you provide evidence or additional experiments demonstrating the model's ability to generalize its reasoning capabilities to novel tasks or attributes not explicitly covered during training? For example, if specific reasoning tasks (such as color attribution) were excluded from training, does the model effectively adapt and correctly handle these tasks during inference? This again, can highlight the benefits of using the MCoT framework.\n\n4. Could you expand upon the implementation details provided in the paper? Clarifying specific technical choices (e.g., hyperparameter selection, computational resources, runtime considerations) would improve reproducibility.", "limitations": "The limitations section could be expanded further by explicitly discussing specific scenarios or failure cases where the proposed method and model might underperform or fail entirely.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The primary research question, which explores the use of multimodal chain-of-thought (MCoT), is compelling and worthwhile to investigate.\n\n2. The pipeline developed to synthesize MCoT data for supervised fine-tuning (SFT) can be beneficial for future research.\n\n3. The insights regarding the contribution of the text-based reward model to image generation performance are valuable and interesting, and highlight the potential of multimodal models.\n\n4. The ablation study, which analyzes the impact of different reward model combinations, is informative.\n\nWeaknesses:\n\n1. The authors do not clearly present results immediately following the cold-start phase. Given the substantial high-quality SFT dataset collected for the specific domain, one would expect a significant performance improvement after SFT alone. Therefore, a more appropriate baseline to clearly demonstrate the impact of the reward models would be the model’s performance after the SFT phase.\n\n2. Despite being central to the paper's focus, the efficacy of MCoT as a method remains somewhat uncertain, particularly considering its computational demands. A simpler baseline comparison could involve selecting the best image from multiple generations using image-based reward models.  Additionally, a valuable comparison would involve performing SFT on top-scoring images and then selecting the best result from several generations, to directly assess whether MCoT offers significant advantages over simpler alternatives.\n\n3. The generalization capability of the model is unclear. A significant advantage of chain-of-thought methods in large language models is their ability to learn reasoning processes that generalize to novel tasks and scenarios. It remains uncertain whether this model can generalize beyond its specific training data. For example, if the SFT data and prompts during RL would not include prompts with color attribution, it is unclear if the model will learn to adjust color attributes effectively during inference.\n\n4. The implementation details provided in the paper are somewhat limited and could benefit from further elaboration to facilitate clearer understanding and reproducibility.", "was_revised": true, "final_justification": "The rebuttal response answered my concerns and therefore I increase the rating to borderline accept.", "rebuttal": "Thank you for your valuable feedback. We address each of your points individually below.\n\n\n### **Q1. The results of SFT's model**\n\nThank you for the insightful suggestion. We have accordingly supplemented Supervised Fine-Tuning (SFT) results, presented in the table below. While SFT yields a modest improvement **(+7)**, the dominant gain **(+15)** emerges from the reinforcement-learning phase. This outcome substantiates that RL is **significantly more effective** than SFT in transferring the model’s visual-understanding capabilities into the image-generation process.\n\n|                | **single obj** | **Two obj** | **Counting** | **Colors** | **Position** | **Color Attr.** | **Overall** |\n| :------------: | :------------: | :---------: | :----------: | :--------: | :----------: | :-------------: | :---------: |\n|    Emu3-Gen    |      0.98      |    0.71     |     0.34     |    0.81    |     0.17     |      0.21       |    0.54     |\n| Emu3-Gen(SFT ) |      0.98      |    0.74     |     0.39     |    0.81    |     0.50     |      0.24       |    0.61     |\n|     Vinci      |      0.99      |    0.86     |     0.48     |    0.83    |     0.86     |      0.54       |    0.76     |\n\n### **Q2. Compare with Best of N**\n\nWe have additionally compared Vinci against Best-of-N baselines; Where Emu3(w.sf&Best-of-N) is fine-tuned with the same cold start data and generated with the Best-of-N (N=16) strategy. The results, summarized in the table below, show that Vinci retains **a clear advantage**. Best-of-N merely re-ranks fixed outputs without enhancing the generator itself, whereas our RL formulation transfers the model’s intrinsic understanding directly into the generation process, yielding **sustained improvements** in quality.\n\n|                         | **single obj** | **Two  obj** | **Counting** | **Colors** | **Position** | **Color Attr.** | **Overall** |\n| :---------------------: | :------------: | :-------: | :----------: | :--------: | :----------: | :-------------: | :---------: |\n|         SDXL[1]         |       -        |     -     |      -       |     -      |      -       |        -        |    0.65     |\n|     Emu3-Gen(SFT )      |      0.98      |   0.74    |     0.39     |    0.81    |     0.50     |      0.24       |    0.61     |\n| Emu3(w.SFT & Best-of-N) |      0.99      |   0.79    |     0.43     |    0.84    |     0.59     |      0.34       |    0.66     |\n|          Vinci          |      0.99      |   0.86    |     0.48     |    0.83    |     0.86     |      0.54       |    0.76     |\n\n>[1]A General Framework for Inference-time Scaling and Steering of Diffusion Models\n\n### **Q3. The generalization capability**\n\nThank you for the insightful suggestion; we have conducted the requested experiment, with results presented in the table below. The model achieves **50 %** accuracy on color_attr which never explicitly seen during training, and the performance is close to Vinvi **(54 %)**, demonstrating that the MCoT framework **successfully generalizes** its reasoning capabilities to novel visual properties.\n\n|                              | **single obj** | **Two  obj** | **Counting** | **Colors** | **Position** | **Color Attr.** | **Overall** |\n| :--------------------------: | :------------: | :-------: | :----------: | :--------: | :----------: | :-------------: | :---------: |\n|             Emu3             |      0.98      |   0.71    |     0.34     |    0.81    |     0.17     |      0.21       |    0.54     |\n|  Emu3(SFT) (w.color_attr）   |      0.98      |   0.76    |     0.43     |    0.81    |     0.53     |      0.28       |    0.63     |\n| Emu3(SFT) (w/o.color_attr）  |      0.98      |   0.75    |     0.43     |    0.79    |     0.53     |      0.22       |    0.62     |\n| Emu3(SFT+RL)(w/o.color_attr) |      0.99      |   0.83    |     0.49     |    0.80    |     0.85     |    **0.50**     |    0.74     |\n|            Vinci             |      0.99      |   0.86    |     0.48     |    0.83    |     0.86     |    **0.54**     |    0.76     |\n\n### **Q4. More implementation details**\n\nTo accommodate longer interleaved sequences, we extend the model's context length to 15,360 tokens. Each image is 512×512 in resolution, corresponding to 4,096 visual tokens.\n\nThe training is conducted on 16 A800 GPUs, and consists of two stages:\n\n● In the cold start stage, we use a batch size of 64, a learning rate of 1e-5, and weight decay β=0.01. This stage lasts approximately 20 hours, allowing the model to acquire basic multimodal generation capabilities.\n\n● In the reinforcement learning stage, we set the group size to 8 and train for an additional 60 hours, refining model performance through reward-based optimization.\n\nPlease refer to the supplementary material for more details, and we will also publish the code and data to ensure reproducibility of the work."}]

Author rebuttal:

Meta-review: This paper introduces Vinci, a framework for text-to-image generation that integrates Multimodal Chain-of-Thought (MCOT) reasoning with RL. The core is the use of a unified autoregressive model that interleaves image generation and understanding, allowing for a "deep thinking" process of iterative refinement. The authors propose a momentum-based reward function to stabilize the RL training process and demonstrate a +22% improvement over the base model on the GenEval benchmark. The work is relevant to the NeurIPS community as it addresses the critical challenge of enhancing the reasoning and self-correction capabilities of generative models.

The paper received four positive-leaning reviews (5, 4, 4, 4) and one borderline negative review (3). After the author rebuttal, all four of the initially positive-leaning reviewers confirmed their ratings, and one raised their score. The initially negative-leaning reviewer did not engage beyond the acknowledgement. Furthermore, the reviewers were generally confident in their assessments (4, 4, 3, 3, 4).

Reviewers acknowledged the conceptual novelty of integrating MCOT within a single generative model and the innovative momentum-based reward function. The significant improvement on benchmark datasets is also highlighted as a major strength.

Initially, several reviewers raised concerns about the limited evaluation scope (only GenEval), the lack of comparison with certain state-of-the-art models, and the reliance on synthetic data for the cold-start phase. There was the initial "borderline reject" rating from one reviewer, who was concerned about the evaluation scope and potential for reward hacking. However, the other reviewers, while acknowledging these as limitations, felt the novelty and strong results outweighed these concerns. The authors' extensive rebuttal, which included new experiments on additional benchmarks and a human evaluation, successfully addressed most of these concerns for the other reviewers.

The proposed momentum-based reward function is well-motivated - the authors also provided a theoretical analysis of its stability and convergence properties in the rebuttal, which was well-received.

The initial experiments on GenEval were strong, and the authors significantly strengthened their empirical validation during the rebuttal by adding results on DPG-Bench and T2I-CompBench, as well as human evaluation data. They also included important ablation studies and comparisons with additional baselines as requested by the reviewers. Also, authors report a practical trade-off: runtime and memory as an overhead of multi-round “deep thinking”. While the code and data were not available at the time of review, the authors have committed to releasing them with the camera-ready version. The paper provides a good level of implementation detail, which was further clarified in the rebuttal.

The authors initially omitted comparisons to some relevant RL-based text-to-image models but added these during the rebuttal, demonstrating the advantages of their approach.

This work has the potential to have a nice impact on the sub-area of multimodal AI. By demonstrating a successful method for enabling a generative model to "think" and iteratively refine its own output, this paper opens up new avenues for research into more controllable and reliable generative models. The proposed techniques could be influential for the development of future unified multimodal systems. The proposed method is also sound and supported by strong empirical evidence, which was substantially strengthened during the author-reviewer discussion period. The work is a valuable contribution to the field of multimodal AI.  The authors must incorporate the additional experiments, analyses, and implementation details from the rebuttal into the final paper.
Decision: Accept (poster)