Interleaving Reasoning for Better Text-to-Image Generation
Authors: Wenxuan Huang
Shuang Chen
Zheyong Xie
Shaosheng Cao
SHIXIANG TANG
Yufan Shen
Qingyu Yin
Wenbo Hu
Xiaoman Wang
Yuntian Tang
Junbo Qiao
Hangyu Guo
Yao Hu
Zhenfei Yin
Philip Torr
Yu Cheng
Wanli Ouyang
Shaohui Lin
Venue: ICLR 2026 Poster (2026)
Keywords: Interleaving Reasoning
Text-to-Image Generation
Primary area: foundation or frontier models, including LLMs

Abstract: Unified multimodal understanding and generation models recently have achieve significant improvement in image generation capability, yet a large gap remains in instruction following and detail preservation compared to systems that tightly couple comprehension with generation such as GPT-4o.
Motivated by recent advances in interleaving reasoning, we explore whether such reasoning can further improve text-to-image (T2I) generation. 
We introduce Interleaving Reasoning Generation (IRG), a framework that alternates between text-based thinking and image synthesis: the model first produces a text-based thinking to guide an initial image, then reflects on the result to refine fine-grained details, visual quality, and aesthetics while preserving semantics. 
To train IRG effectively, we propose Interleaving Reasoning Generation Learning (IRGL), which targets two sub-goals: (1) strengthening the initial think-and-generate stage to establish core content and base quality, and (2) enabling high-quality textual reflection and faithful implementation of those refinements in a subsequent image. 
We curate IRGL-300K, a 300K-scale dataset organized into six decomposed learning modes that jointly cover learning text-based thinking, and full thinking–image trajectories. 
Starting from a unified foundation model that natively emits interleaved text–image outputs, our two-stage training first builds robust thinking and reflection, then efficiently tunes the IRG pipeline in the full thinking–image trajectory data. 
Extensive experiments show SoTA performance, yielding absolute gains of 5–10 points on GenEval, WISE, TIIF, GenAI-Bench, and OneIG-EN, alongside substantial improvements in visual quality and fine-grained fidelity. 
As an early exploration, our results demonstrate that interleaving reasoning is a powerful paradigm for advancing T2I.
The code, model weights and datasets will be released in: https://github.com/Osilly/Interleaving-Reasoning-Generation.

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INTERLEAVING REASONING
FOR BETTER TEXT-TO-IMAGE GENERATION
Wenxuan Huang* Shuang Chen', Zheyong X Shaosheng Cao ,Shxxinng Tang", Yufan Shen', Qingyu Yin', Wenbo Hu" Xiaoman Wang', Yuntian Junbo Qiao'.
Junbo
Yue Guo Yao Hu Zhenfei Yin Phillip Torr", Yu Cheng?: Wanli Ouyang', Shaohui Lin
East China Normal University "The Chinese University of Hong Kong *Xiaohongshu Inc.
4 University of California, Los Angeles *Zhejiang University University of Oxford
7 Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE, China
wxhhang@gga com (Wenxuan Fuang)
shaohui wtt.miiiiin07m@ma...mcmmm com (Shaohui Lin)
Github
com http:://g.......coooooo/////ttee Reasoning Generation
(a)
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Interleaving Reasoning
Gendi bonch
OraSS-EN
As shown in (a), we illustrate an example of Interleaving Reasoning Generation (IRG). Given prompt, the model first produces text-based reasoning process and then generates image conditioned on that reasoning. Next. building upon the initial image, the model reflects on how to improve quality and produces a refined image through this reflection process. IRG can substantially enhance image generation quality. For instance, in the top case of (a), IRG improves upon the previous generated image via multi-ture reasoning, enhancing rendering textures, shadow realism, and other visual properties. In the bottom case of (a), IRG significantly improves fine-grained details, such as the delicate structures of fingers-highlige As shown in (c), compared to current SoTA models, our proposed IRG achieves clearly superior performance across multiple mainstream T2I benchmarks.
Figure 1:
within the red box (as detailed in (b)).
"CCoresponding authors.

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ABSTRACT
Unified multimodal understanding and generation models recently have achieve significant improvement in image generation capability, yet a large gap remains in instruction following and detail preservation compared to systems that tightly couple comprehension with generation such as GPT-4o. Motivated by recent ad- vances in interleaving reasoning, explore whether such reasoning can further improve Text-to-Image (T2I) generation. We introduce Interleaving Reasoning Generation (IRG), framework that alternates between text-based thinking and image synthesis: the model first produces a text-based thinking to guide an initial image, then reflects on the result to refine fine-graines details, visual quality, and aesthetics while preserving semantics. To train IRG effectively, we propose Inter- leaving Reasoning Generation Learning (IRGL), which targets two sub-goals: (1) strengthening the initial think-and and-annnraatte stage to establish core content and base quality, and (2) enabling high-quality textual reflection and faithful imple- mentation of those refinements in subsequent image. We curate IRGI-300K, a 300K-scale dataset organized into six decomposed learning modes that jointly cover learning text-based thinking. and full Lanking-image trajectories. Starting from a unified foundation model that natively emits interleaved text-image outputs, two-stage training first builds robust thinking and reflection, then efficiently tunes the IRG pipeline in the full thinking-image trajectory data. Extensive ex- periments show SoTA performance, yielding absolute gains of 5-10 points on GenEval, WISE, THIF, GenAl-Bench, and OneIG-EN, alongside substantial im- provements visual quality and fine-grained fidelity. As an early exploration, our results demonstrate that interleaving reasoning is a powerful paradigm for advancing T2I. The code, model weights and datasets will be released httpp: Eigithuub..m wwt.c//mossooo.. Os / Inter www.nerrllaviie Reeigg-eeeooniin iigonninniggooo Genggg-eeerriiioo
INTRODUCTION
Unified multimodal understanding and generation models consolidate image/text understanding and image synthesis capabilities within a single foundation model, and have recently emerged focal
point of interest in the research community (Sun et al. 2024| Team 2024 Tong et al. 22024] Wu et al.
2025a Xie et al.
2024| Chen et al. 2025a Xiao et al. 2025b a Liao al., [025]] Xie et al. 2025b;
Wu et al. 2025b Deng et al. 2025 Representative efforts in this line of research, exemplified by
GPT-4o (OpenAl| 2025c), seamlessly integrate comprehension and generation capabilities. This
enables pronounced performance gap relative existing unified models, particularly in instruction- following for image generation and in the preservation of visual details (Deng et al. 2025).
Motivated by recent advances in text-based reasoning, notably test-time scaling techniques for (Multimodal) Large Language Models ((M)LLMs) (Jaech et al.] 2024) /uo et al., 2025a Huang al. 2025| Chen al. 2025b), a growing body of work has explored whether incorporating such text-based reasoning processes can yield improvements in the fidelity and overall quality of image generation (Fang et al. 2025a; Xiao et al. 2025b; Deng et al. 2025 Jiang et al. 2025). These works underscore this perspective and seek to exploit large-scale interleaved text-image corpora to learn subtle cross-modal interaction patterns, thus enabling seamless knowledge transfer between the understanding and generation stages of the model. However, in Text-to- Image (T2I) tasks, they employ only a single textual segment as auxiliary supervision in T2I generation, with the objective of producing outputs that more faithfully adhere to the original prompt. Recently, some works in (M)LLM field have focused on the interleaving reasoning, i.e., multi-turn interactions and cxhibits sophisticated reasoning dynamics (Huang) (2025). while this reasoning modality has empirical demonstrated superior accuracy in addressing complex problems (OpenAl 2025b a). This observation motivates the exploration:
Whether interleaving reasoning can further enhance T21 generation quality?
As shown in Fig I in generating prompt-aligned image, the model is typically able to produce content that is broadly correct in terms of semantics, yet it remains challenging to attain superior visual quality and fine-grained fidelity (e.g., in rendering textures, shadow realism, and delicate
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structures such fingers). The idea is intuitive: if high-quality image synthesis is considered hard problem, adopting a multi-step reasoning strategy to tackle it is both reasonable and necessary. To solve this, straightforward idea is to have the model first produce text-based thinking process and generate an image based on that reasoning. Then, building on the initial image, the model reflects how to improve its quality, and produces improved image through reflection. We denote this process as Interleaving Reasoning Generation (IRG). Thus, we argue two points, I) an additional text-based reasoning process can serve auxiliary supervision for image generation, thereby alleviating the difficulty of direct generation, and 2) producing one image that simultaneously attains high visual quality and precise instruction following is non-trivial, whereas a multi-turn generation strategy can incrementally refine the output toward the desired goal. While these positions are aligned with prior reflection-basse based T2I generation approaches, the key distinction lies in their goals: prior methods (Tian et al. 2024 Zhuo et al., 2025) WW et al., 2025h) Chern et al., 2025) generally employ reflection to rectify major semantic or structural errors in the generated content, with some adopting non-end- to-eddto-d frameworks, whereas our approach focuses on leveraging reflection to refine fine-grad tne-graiied details and improve overall visual quality in an end-to-end manner, with the main subject matter established during the initial generation. Specifically, through IRG, we not only enhance the semantic correctness of the generated content, but also focus on improving the quality, fine-grained details, and aesthetic aspects of the generated images.
Based on the insights, we firstly select a unified multimodal understanding and generation model as the base model, given its capability produce interleaved text-image outputs. To facilitate intericaving reasoning-basee
generation with reflection, we propose the Interleaving Reasoning Generation Learning (IRGL) paradigm and formulate the objective as two sub-goals. The first is to strengthen the model's 's initial thinking and generation stage, which establishes the core content and base quality of the generated image. The second is to equip the model with the ability to produce detailed, high-quality text-based reflections, and to generate enhanced images that faithfully implement those reflections. particular, we propose the IRGL-300K dataset, which refines the aforementioned two objectives into two complementary focuses: learning text-based thinking and mastering the image generation with thinking pipeline. It comprises six decomposed learning modes that jointly target comprehensive enhancement of model performance throughout the IRG process, while ensuring optimal exploitation of data resources. In the training pipeline, the model is first trained to generate accurate initial thinking given prompt, well to produce text-based reflections based on the initial reasoning step that improve quality. Furthermore, we incorporate full thinking-image trajectories to mitigate potential degradation in the model's core generative ability in this training stage. Then, leveraging the acquired thinking generation capability, we utilize the full thinking-imann trajectory data to efficiently train the entire IRG pipeline.
Extensive experiments demonstrate that our proposed IRG achieves Statc-ofThhe Art (SoTA) bench- mark performance, delivering absolute improvements of 5-10 points across multiple benchmarks, including GenEval, WISE, THF, GenAl-Bench, and OneIG-EN. In addition, IRG significantly en- hances visual quality and fine-grained fidelity. As an early effort to introduce Interleaving Reasoning into the T2I domain, we hope our work can inspire future research in this direction.
2
METHOD
2.1 PRELIMINARIES AND NOTATIONS
Unified multimodal understanding and generation models based on an integrated transformer ar- chitecture, e.g., BAGEL (Deng et al.) 2025). jointly perform image understanding and generation within a single architecture. facilitated by large-scale training on interleaved text-image data. Such models can naturally handle both interleaved inputs and interleaved outputs. Let Tin and 4n represent the input text and image, respectively, while (-()) and ( denote the initial output text and image, respectively. The standard image understanding and T2I generation with self-CoT reasoning process can be formulated as:
Image understanding: Tmn + Im + outs
T21 generation with self-CoT: Tin -> out out-
Previous works that adopt self-CoT (Deng et al.) 2025) Fang et al., 2025a Xiao et al., 2025b Jiang et al. 2025) typically focus solely on text-based reasoning to improve image generation, while
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Training
Unified Model
Unified Model
Initial
Prompt
Dunill Thosk
Zmggriving Think. Learniy
Unified Model
Unified Model
Yrage
Frompt
Zonty Those
Think
Zmprowiyy Thank
ster Lrondng
S9p Leannit
Unified Model
Unified Model
Promept
Zerrial Think
Initial Imagee
Prongt
Znttill Think
VIT-WAE
Emproving Think
Teargved Targe Improsiny
Inference
Input
Unified Model
Generated part
Computing loss
Promes
Initial Think
Iritial Image
VITWWAE
Improving Think
Impreved Image
CFG
CFG condition
CFG Image Coche
OFG Text Coche
Forward inference
Figure 2: Overview of proposed IRG training and inference pipeline. IRG learns the text- bast-based thinking process and the complete high-quality image generation pipeline under six decomposed learning modes. During inference, introduce dedicated CFG condition design (Ho & Salimans/ 2022) for IRG's improved image generation steps. The "Input part" denotes the inputs to the IRG module, comprising the original prompt, ViT and VAE features of the image, and our constructed queries. "The Generated part" refers to the outputs produced by IRG, including the intermediate text- based reasoning process and the synthesized image. "Computing loss" indicates which components participate in the loss computation during training.
overlooking the potential leveraging the initially generated image to further enhance visual quality and perform multi-step information fusion for better results. Thus, to conduct additional T21 reasoning step conditioned on the previously generated text and image, we first encode the generated image into Vision Transformer (ViT) features and Variational Autoencoder (VAE) features, which are defined as
1y. Then, based on the full information cache, the multi-turn generation can be conducted and the pipeline denoted as follows:
Tin ( (1)) eDC 11"
Tout
ene
T
[(m]
where means the total number and cnc. represents the image feature encoding process, and note that for intermediate images generated at the k-th turn (e.g., y(k), ), only their encoded representations (k) propagated to the subsequent computation stages. During the generation of the final image
((n) the model exchanges and exploits multiple segments of interleaved text-image representations, a process we term Interleaving Reasoning Generation (IRG). By formulating the synthesis of the final image as the ultimate goal, the model employs multi-stage progressive generation and cross-modal information fusion to maximize output quality. We note that an inherent strength of unified models lies in their capacity process and generate interleaved inputs and outputs, inherently supporting multi-turn generation with self-CoT reasoning. This property enables us to investigate how interleaving reasoning within the generation pipeline can further extend the achievable limits of generative capability.
2.2
INTERLEAVING REASONING GENERATION
2.2.1
OVERVIEW
As mentioned above. IRG can be defined as comprising two components: (1) an initial text-based reasoning process followed by image generation based that reasoning, and (2) repetition of the
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first component produce improved image. In this work, we focus solely single refinement iteration, i.e., limiting the second component to one turn (set n in Eq. 2 to 2), in order to validate hypothesis whether Interlcaving Reasoning can effectively enhance text-to-image generation quality.
In the following sections, we detail the Interleaving Reasoning Generation Learning (IRGL) framework and explain how different forms of interleaving reasoning data can be effectively utilized perform hierarchical learning with distinct emphases, as elaborated in Sec. 2.2.2 Furthermore, in Sec. [.2.3] we introduce the data construction pipeline of IRGL-300k dataset, while in Sec. 222,4 we simply describe the inference strategy of IRG, such as the Classifier-Free Guidance (CFG) (Ho & Salimans 2022) condition designs.
2.2.2 INTERLEAVING REASONING GENERATION LEARNING
When we set n to 2, Eq. 2 will be reformulate as:
Tm (
+ enc I(11 - T
out"
It can be observed that when aiming to enhance the quality of the final image T in IRG pipeline, we decompose the process into four progressive intermediate steps: (1) ensuring the correctness of the initial thinking process T (2) improving the quality of the initial generated image (2) (3)
generating an accurate aaproving-thinking step T(2) based on the first image to guide the production
of a better image, and (4) synthesizing the final high-quality image y(2) by integrating all preceding decision steps. Evidently, as shown Fig 2 various decomposed learning modes can designed to improve the model's intermediate reasoning capacity. We begin with the enhancement of the initial reasoning step. i.e., above steps (1) and (2), which can be instantiated as the following tasks:
Initial Thinking Understanding Learning: In this task, we aim for the model to learn how to generate the correct initial thinking process given original prompt T', and prior image features 1 111) The design insight behind this task is follows: when the model is provided with both a prompt and an image consistent with that prompt, we construct an auxiliary supervision, how to
question Tg in which the model learns, through produce a reasoning process aligned with the prompt and to recognize what kind of image such reasoning process would yield.
Ti 1 + out
(4)
Initial Thinking Generation Learning: This task directly imitates the reasoning process for generating initial thinking given the original prompt, and can be considered a more challenging task compared to Initial Thinking Understanding Learning.
Te
Initial Full Learning: In this full initial reasoning learning setting, the model learns from both text-based reasoning sequences and high-quality image data to enhance the quality of its initial image generation, thereby providing stronger foundation for producing improved images in subsequent reasoning stages.
Tin > T())
(1) out
(6)
Furthermore, we also design three tasks to learning how to generate the improving thinking and improved image based on the initial reasoning step (the above steps (3) and (4)):
Improving Thinking Understanding Learning: This task is closely related to Initial Thinking Understanding Learning, but focuses enabling the model to, given a prompt, learn to generate the improving thinking process for enhancing an initial image to an im- proved image. This achieved by understanding the prompt T... comparing the differences between the features of the initial 111) and improved images r(2) and by answering carefully designed questions Tg-
Ten + 1411 + 152) + Tg + (22)
(7)
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Improving Thinking Generation Learning: This task builds on the initial reasoning stage, focusing on learning how to generate improving thinking.
Tin + T + 170 -
(8)
Improving Full Learning: This task represents complete IRG process, but we constrain the model, under the condition that the initial reasoning is already completed, to learn only the improving reasoning and the high-quality improved image components. As the most crucial stage of IRG, the model must learn to identify the differences in visual quality and fine-grained fidelity between the two images, and to leverage this understanding to generate the optimal image during the improving reasoning step.
Tin + out
f 1)
(2) [(2) out
out
The aforementioned decomposed learning modes can he clearly divided into two training objectives: (1) learning the text-based thinking process (Initial Thinking Understanding Learning, Initial Think- ing Generation Learning, Improving Thinking Understanding Learning, and Improving Thinking Generation Learning), and (2) learning the complete high-quality image generation pipeline under the auxiliary supervision of the reasoning process (Initial Full Learning and Improving Full Learning). This design likewise addresses the limited availability of high-quality, full IRG thinking-iman trajectories data, while learning from text-based reasoning serves as a partial remedy to this problem.
Furthermore, we employ a two-stage training pipeline. In stage 1, the model is optimized on all six tasks to generate accurate initial reasoning from given prompt and to produce text-based reflections derived from the initial reasoning step to enhance output quality. The main goal this stage is to strengthen the text-based reasoning capability, while incorporating full thinking-image trajectories to avoid degrading the core generative performance. Empirically, we find that this cessoning-focussee training converges relatively rapidly. In the second stage, leveraging the thinking generation ability learned in Stage I, we employ full thinking image trajectory data (i.e., data in Initial Full Learning and Improving Full Learning) efficiently optimize the entire IRG pipeline. In this training stage involving image generation, convergence demands more iterations, since the model must spend additional training time learning the fine- mne-gra fidelity transformations from the initial image the improved image.
Discussion: Unfortunately, constructing complete interleaving IRG data is non-trivial, even when considering only the two-turn case, due to the following two challenges. First, obtaining final high-quality images inherently difficult, as the quality of existing open-source T2I datasets remains suboptimal. This limitation has motivated many recent works to distill images generated by powerful models such as GPT-4o (Chen et al. 2025a Wu et al., 2025b). Second, although a subset of high-quality data from GPT-4o is available, IRG requires paired samples linking an initial image to its improved counterpart. Designing the transformation process from the initial image to the improved image is itself a challenge, which means that such pairs must be constructed from scratch and cannot be directly derived from GPT-4o-distilled T2I data. These two issues make it difficult to obtain complete IRG datasets at scale. To mitigate the scarcity of fully optimized training data, we design multiple intermediate training objectives. This is because we only learn the text-based thinking process during intermediate objective training, in order avoid low-quality image data pollution. We expect that, when conditions permit, access to a large quantity of complete IRG data would lead even better performance.
2.2.3 INTERLEAVING REASONING DATA CONSTRUCTION
This section introduces the IRGL-300k dataset construction pipeline for the aforementioned six decomposed learning modes. We utilize one MLLM and the strong image generation model to construct the dataset for training.
Data for learning the initial reasoning step. For the Initial Thinking Understanding Learning task, we construct training data from open-source T2I datasets containing prompt-image pairs. First, design an initial thinking template, and then instruct a large language model (e.g., Qwen2.5-VI. (Bai ct al. 2025b)) to generate reasoning process that is consistent with both the prompt and the corresponding image from the original T21 data. Finally, we organize the data according to Eq. 4
the prompt corresponds to T the image is encoded to obtain features T(1), manually designed

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Table 1: Evaluation of text-to-image generation ability on GenEval benchmark. 'Gen. Only" stands for an image generation model, and 'Unified' denotes a model that has both understanding and generation capabilities. refer to the methods using MLLM rewriter. * means report the reproducing results using the official Github repository and checkpoint. The best Overall results are bolded.
Type
Model
Single Obj.
Two Obj.
Counting
Colors
Position
Color Attri.
Overall
FixAAr--
Ches
SDv2.1
[Romisch e
2099
0.98
0.51
0.44
0.85
0.07
0.17
0.50
Gov. Only
DALL-E
Kamssu or
0194
(.06
0.49
0.77
0.10
0.19
0.52
Emu3 Gen
0.51
0.34
0.17
0.21
0.54
Po
DALL-E
Belke 20 2025
0.96
0.87
0.47
0.83
0.43
0.45
0.67
SD3 Medium
0.99
0.94
0.72
0.89
0.33
0.50
0.74
FLUX.I-dev"
2124
Chaneleon Team 2024
0.39
LWM
0.15
0.47
SEED-X
0.14
TokenFlon-X
X1 Wirt XP24
0.95
0.60
0.41
0.81
0.16
0.24
0.55
I.J.UME
Want of AT740
0.99
0.86
0.45
0.71
0.39
0.28
0.61
0.42
0.61
Tranfusion
0.63
Ginlled
Fmm3-GGen
Wane et
11246
0.99
0181
0.42
0.80
0..99
0.45
0.66
X
JamusPro-778
Musn of
0.59
0.90
0.79
0.66
0.50
MetiQuery-Xi
XI
(iin of
20135
0.50
BAGIEL
0.60
Show-o?
0.92
0.62
BAGEL w// self-CoT* [Dagg 2025
0.75
0.89
0.54
0.63
0.79
0.73
GPT-4o OpenAl 2025e
0.99 0.92 0.85
0.92
0.75
0.61
0.84
understanding question (e.g., "You have been given one prompt and one image ..."" corresponds to TG, and the MLLM-generated initial thinking corresponds to T-(1)
For the Initial Thinking Generation Learning, the initial thinking acquisition pipeline is similar to the Initial Thinking Understanding Learning task. uses the prompt and MLLM- initial thinking to obtain the train date (as Eq. 5).
For the Initial Full Learning data, ensure learning from high-quality image information, we input the original prompt into high- image generation model (GPT-4o (OpenAl (2025d)) to produce
high-quality image, which serves T!ll in Eq. 6 The initial thinking is obtained in similar manner to that described above, by providing the prompt and the GPT-4o-distilled high-quality image an MLLM.
Data for learning the improving reasoning step. For generating data for the improving reasoning step, we encounter a key challenge: given access to a high-quality image to serve as the improved image, must determine the source of the initial reasoning step data. We choose to data generated by the base model (i.e., e. BAGEL (Deng et al. 2025)) conditioned on the same prompt as the initial reasoning step data. This design decision is motivated by two considerations: (1) provides a simple and efficient way to obtain multi turn IRG data at scale, and (2) it allows us to improve the model's 's performance without compromising the original capabilities of the base model.
For the Improving Thinking Understanding Learning task, we first input the prompt into the base model to generate the initial thinking and the corresponding initial image. We then design an instructional prompt to guide the MLLM in generating improved thinking based on the base model's generated image and the image from the T2I dataset. The model is instructed to produce the improving thinking according to a predefined template. We adopt stage-level template (Xu et al. 2024). which requires the model to first generate a part of the analysis previous generated image issues. Then, "Step-by-Step
generate the stages in the format "Detailed Explanation of Required Improvements: Modification Guidance: ...", and "Final Comprehensive Prompt for the Improved Image: ...". Finally, we organize the original prompt T;, and initial image generated by base model 7("), the image in the T2I dataset prompt and images... ...") Ty and the improving thinking (T(2) by Eq.
1(2), manually designed understanding question (e.g.. "You have been given one
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Table 2: Comparison of world knowledge reasoning WISE. WISE examines the complex semantic understanding and world knowledge for T2I generation. 'Gen. Only' stands for an image generation model, and 'Unified' denotes model that has both understanding and generation capabilities. * means we report the reproducing results using the official Github repository and checkpoint. The best results are bolded.
Type
Model
Cultural
Time
Space
Biology
Physies
Chemistry
Overall
SDv1.5
Rombach et al 2022
0.34
0.35
0.32
0.28
0.29
0.21
0.32
Gen. Only
SDXL
Foweell et al. 0029
0.43
0.48
0.47
0.44
0.45
0.27
0.43
SD3.5-larg
Essey
2024
0.44
0.50
0.58
0.44
0.52
0.31
0.46
Alphr
hen 21.4
0.45
0.50
0.48
0.49
0.56
0.34
0.47
playground-v2.3
Lre 20240
0.49
0.58
0.55
0.43
0.48
0.33
0.49
FLUX.1-dev Labs 2024
0.48
0.58
0.62
0.42
0.51
0.35
0.50
Jamus (Wr et al
2025a
0.16
0.26
0.35
0.28
0.30
0.14
0.23
VILA-U Wa (aakUUWWWeea
2024
0,26
0.33
0.37
0.35
0.39
0,23
0.31
Show-o-511
[ et
M114
0.28
0.40
0.48
0.30
0.46
0.30
0.35
Jamus-Pro-7B Chen
211350
0.30
0.37
0.49
0.36
0.42
0,26
0.35
Unifeed
Emu33 Wang e1 E022445
0.34
0.45
0.48
0.41
0.45
0.27
0.39
MetaQuery-XL
20155
0.56
0.55
0.62
0.49
0.63
0.41
0.55
BAGEL
Denni el
0.44
0.55
0.68
0.44
0.60
0.39
0.52
Show-o2"
20256
0.64
0.58
0.61
0.58
0.63
0.49
0.61
BAGEL self-CoT (Deng
2025
0.76
0.75
0.65
0.75
0.58
0.70
IRG (Ours)
0.78
0.72
0.76
0.81
0.82
0.78
0.77
GPT-lo OpenAAl 20256
0.81
0.71
0.89
0.83
0.79
0.74
0.80
For the Improving Thinking Generation Learning task, the data is same as Improving Thinking Understanding Learning, while use the initial thinking generated by base model as T Eq.
In the Improving Full Learning setting, GPT-4o is used to produce the improved image in the IRG trajectory. Given the prompt and the initial image from the base model, GPT-4o generates higher-quality, prompt-consistent image, which we adopt as the improved image r(2) in Eq. 9 An MLLM is then employed to produce stage-level improving thinking detailing the transformation from the initial image to the improved image.
2.2.4 INFERENCE STRATEGY
As shown in Fig the model produces a "text- text- trajectory. This poses chal- lenge: conventional diffusion- based generation models, the CFG-conditioning design is typically straightforward, such as directly comparing the presence versus absence of the prompt. In contrast, for our proposed IRG under even only two-turn reasoning pipeline, particularly before generating the improved image thageetherreae are four possible condition sources to compare (i.e., the prompt, the initial rea- soning, the initial image, and the improving reasoning). Therefore, a customized CFG-conditioning strategy is required. Based this, we adopt the framework incorporates two complementary CFG condition schemes: (1) conditioning with versus without the image information from the initial generation, and (2) conditioning with versus without the reflection text. In practice, set the the guidance scale syper-parameters in CFG Image condition (versus without the image information) and CFG text condition (versus without the text information) to 2.0. This strategy helps maintain high visual quality and fidelity in images produced during the improving reasoning steps, with notable benefits for generation stability (see Fig
EXPERIMENTS
Please refer to Appendix to obtain the detailed data and training settings. We provide the main and Appendix C and then we introduce the detailed ablation study, the result analysis and the visualization in Sec. 3.2 and Appendix
results of IRG in Sec. 3.1 and
3.1 MAIN RESULTS
Across mainstream benchmarks (e.g., GenEval (Ghosh et al. 2023) and WISE (Nuu et al. 2025) ).
IRG consistently surpasses strong unified baselines and colf-CoT variants, evidencing benefits of interleaving reasoning with generation beyond any single metric suite. On GenEval (Tab. [ IRG achieves the best Overall 0.85 (+0.07/0.066 over BAGEL w/ self-CoT)), with strong subskills in
8

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Table 3: Ablation study of IRG. The base method is BAGEL w/ self-CoT. "High-quality Images Training" means that we directly use the image data of Initial Full Learning and Improving Full Learning (see Sec. 2.2.2) to train the base model. "Interleaving Reasoning Generation" indicates that using the full IRG binking-image trajectories during training. "Decomposed Learning Modes" means that we train the model in decomposed learning modes with two-stage training.
Model
WISE
TIIF
GenAI-Beuch
BAGEL self-Col Deng al. 2025
0.70
68.06/68.76
0.81
+ High-quall quality Images Training
0.73
70.69/69.85
0.80
Interleaving Reasoning Gencration
0.76
73.90/11.37
0.83
Decompused Learning Modes (Ours)
0.77
76.00/3.77
Table 4: Analysis of single-turn and multi-turn IRG pipeline. "IRG reasoning step 1" means that the initial image generated by IRG as the evaluation images.
WISE
THI
GenAl-Bench
Model
Score
Rank soore
Score
Score
Owen
GPT-4o
UnifledReward
Avg.
Human
IRC rensoningg step
0.79
7%
17%
75 84/73.00
0,84
IREE (Our))
0.77
57%
76.0777777
Counting 0.83 and Position 0.74. On WISE (Tab. it sets unified SOTA of 0.77 (+0.07 over BAGEL w/ self-CoT),, with consistent gains across domains (e.g., Biology 0.81, Physics 0.82). For more benchmark results, such as TIIF (Wei et al. 2025) (Tab. 5). GenAl-Bench (Li al., 2024a) (Tab. 6), and OnelG-EN (Chang et al.
2025
(Tab. with more detailed experiment analysis, are
presented in Appendix C
3.2 EXPERIMENTAL RESULTS ANALYSIS
Ablation study. Tab. 3 isolates the contributions of data and training objectives. Adding high-quality image training on top of a self-CoT baseline brings moderate improvements (WISE 0.70-+0.73,
and TIIF 68.06/68. 788-70.69/9.888 8.55, while when introduce the IRG pipeline (i.e., using the data of Initial Full Learning and Improving Full Learning) achieve the significant improvement (0.03 benchmark score improvement in both WISE and GenAl-Benchh Furthermore, adopting IRG with six decomposed learning modes yields the largest jump (WISE 0.77. TIIF 76. 76.00/73.77, GenAl-Bench 0.84). This supports our hypothesís that text-only thinking supervision (Sec. 2.2.2) is data-efficient proxy for scarce full IRG thinking-image trajectories, and that mixing text-based thinking learning, and full learning modes provides complementary signals.
Analysis of single-turn and multi-turn IRG pipeline. As shown in Tab. 4 two-turn IRG and the initial generated images achieve similar benchmark scores, and some benchmarks, initial generated image even attains slightly higher scores. However, this does not imply that multi-turn IRG offers no benefits. Our proposed IRG leverages image-conditioned reflection enhance visual quality and fine-grained fidelity.
We evaluate the initial generated image and improved image generated by IRG the WISE bench- mark by prompting a MLLM to directly compare the two images in terms of generation quality, fine-grained details, aesthetics, and other visual aspects. To eliminate positional bias, we ran- domly shuffle the order of the two images presented to the MLLM and repeat the evaluation three times, reporting the averaged results. Multiple MILMS are employed as evaluators, including Qwen2.5-VL-72B (Bai et al. (0255)) GPT-4o (OpenAl) (025c), and UnifiedReward (Wang et al.) 2025b) (using its default pairwise comparison protocol, which incorporates the original task text prompt). This multiple MLLMs as judge method mitigates evaluator-specific biases and indicates better generalization of perceived quality.
The ranking study in Tab. shows that the full IRG pipeline improves agreement with multiple automatic raters compared to the first-step-only variant (average rank score: 63.3% vs. 36.7%), suggesting that two-turn IRG produces images whose improvements consistently recognized by heterogeneous MLLM evaluators.
Furthermore, recruited three professional human annotators to evaluate the image quality generated by IRG at reasoning steps I and on the WISE benchmark. The annotators were instructed to compare the quality of two images based on the original prompt, with the order of images from the two IRG

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rounds randomly shuffled. They were allowed to select from three options: "A is better", "B is better", or "Equally good". As shown in Tab. 4 the human evaluation results demonstrate strong consistency with the MLLM judge results. Notably, human judges for images generated at IRG reasoning step 2, which validates that IRG can significantly enhance image generation quality through multi-turn reasoning.
jumgns exhibit even stronger preference
CONCLUSION
In this paper, we propose the Interleaving Reasoning Generation (IRG) framework, which generates high-quality images through a "text- laagg--tet--mmen
process. Specifically, given a prompt, the model first produces a text-based reasoning sequence and then generates an image conditioned on that reasoning. Next, building upon the initial image, the model reflects on how to improve its quality and produces a refined image through this reflection process. Extensive experiments on mainstream benchmarks demonstrate significant improvements in generation performance.
ACKNOWLEDGEMENTS
This work is supported by the National Natural Science Foundation of China (NO. 62572193), the Open Research Fund of the Key Laboratory of Advanced Theory and Application Statistics and Data Science, Ministry of Education, and the Fundamental Research Funds for the Central Universities, JC STEM Lab of AI for Science and Engineering, funded by The Hong Kong Jockey Club Charities Trust, the MTR Research Funding (MRF) Scheme (CHU-24003), the Research Grants Council of Hong Kong (Project No. CUHK 12H314213224).
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A RELATED WORK
A.1 UNIFIED MULTIMODAL UNDERSTANDING AND GENERATION MODELS
Unified Multimodal Understanding and Generation Models has attracted much attention of the
research community. The mainstream research works can be divide into three categories: (1)
Autoregressive (Wu et al. 2025a Chen et al. 2025c; Lu et al. 2024 Qu et al. 2024 Team 2024
Wang et al.] 2024b). These methods adopt the next token prediction paradign gm to generate the text and
image token in one unified model. (2) Additional Diffusion (Dong al. 2024 Wu al. 2024b Pan
et al. 2025 Tong et al. 2024 Gao et al. 2025). ). They usually combine pre-trained LLM backbone with an external diffusion module. The LLM is then used to obtain the semantic conditions that enable the diffusion module to generate an image. And (3) Unified Integrated Transformer (Deng jet al. 2025| Liang et al. 2024, Ma et al., 2024; Shi et al. 2024 Zhou et al. 2024). this category, works typically integrate the LLM and diffusion models in one transformer model. Our proposed IRG based on the unified integrated transformer model BAGEL (Deng et al. 2025) due to was pre-trained on the large -scale interleaved text-image data. In principle, our proposed IRG framework can be effectively applied to all of the aforementioned types of unified models, as they naturally handle both interleaved inputs and interleaved outputs.
A.2 REASONING MODELS
Text-based reasoning models have achieved significant progress in solving a wide range of real- world
tasks (Jacch et al. 2024; Guo et al., [225a: Huang al. 2025 Chen et al.
2025b d Zeng et al.
2025a Gu et al. 2025 Li et al. 2025 You You et al. 2025 Huang et al.
2026b
Zeng et al. [
et al.] 2025 Feng et al. 2025| Zhan al. [202 Wang et al. 2026
2026 Yin et al. 2025 ?: Zeng et al. 2025b Qi et al. 2025 Zhao et al. 2025 Fang et al. 2025b] Wang et al., 2025a
Tang et al.
Huang et al. 2026a). Recently, several works have begun to adopt interleaving reasoning to address more complex problems (Huang) 2025 OpenAl 2025h a). i.e., incorporating non-text modalities into multi-turn reasoning processes. In the text-to-image (T2I) domain. the latest studies explore whether introducing a text-based reasoning step can enhance image generation performance (Fang al. 2025a Xiao af. 2025b; Deng al. 2025 Jiang et al. 2025). Our IRG framework is inspired by these advances and seeks to integrate interleaving reasoning into the T2I generation process. T2I methods (Tian et al.) 2024, Zhuo et al., 2025; Wu et al.) 2025b; Chern et al., 2025). the key distinction lies in IRG's s dual focus: not only enhancing the semantic correctness of generated content but also improving visual quality, mne-grained fidelity, and aesthetic appeal. Furthermore, compared to Self-Correcting with LLM (Wu et al.] (024c) and CoT-based Image Generation (Guo et al. 2025b) methods, our proposed IRG offers three key advantages: (1) it integrates the reflection process into an end-to-end framework instead of relying on external reward models or LLMs for judgment; (2) it introduces a novel seat--mage-text-imageee
While our approach shares conceptual similarities with reflection-
interleaved reasoning framework that leverages text-based reasoning to guide image generation, rather than solely focusing on image-based reflection; (3) through broad paradigm learning, it achieves significant improvements across multiple diverse benchmarks in both semantic correctness and visual quality.
B EXPERIMENT SETTINGS
IRGL-300k dataset. For the Initial Thinking Understanding Learning, Initial Thinking Generation Learning, Improving Thinking Understanding Learning, and Improving Thinking Generation Lear- ing tasks, we use the open-source T2I dataset OSP1024-286k (Lin et al. 2025). For each of the four tasks, we sample 50K instances from the dataset for data construction.
We employ the GPT-4o-distilled T21 dataset BLII33-600k (Chen et al.) 2025a) in Initial Full Learning. For Improving Full Learning, we construct the dataset by distilling GPT-4o with a curated set of prompts. Specifically, this prompt set is sourced from the training set of T21-compbench (follow- ing (Jiang et al.) 2025)) In addition, for each prompt, we use Qwen3 (Yang et al.) 2025) to generate 1-2 complex prompt variants. Furthermore, following (Chen et al.) 2025a), we construct prompts from common entities and then use Qwen3 to rewrite each into 2-3 complex variants (we do not use the original entity prompts directly to obtain the GPT-4o-distilled images). In total, this procedure produces approximately 30K samples.
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11
12
10
9
3
8
6
KIBBLE
Figure 3: Visualization results of IRG at 1024x1024 resolution. The examples are selected from WISE
NNu et al. 2025). TIIF (Wei al. 2025 and GenAl-Bench (Li al. 2024a).
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Table S: Quantitative evaluation results of instruct-following capability on THI testmini (QwenVL.2.5-7BB as the evaluation). * means we report the reproducing results using the of- ficial Github repository and checkpoint. The best Overall and Avg. results are bolded.
Bonk Pollentag
Aftrnceed Pallontag
Dedigner
Overal
Model
Arst
Ratastiee
Rasooring
<Raswiing
+heessning
Style
World
leeg
10ng
long
kee
RLUX
Lin
004
66.34
66.72
14.41
76.67
12:50
15.90
18,20
79.35 72.32 14.75
60.72
00.55
65.76
65.90 51.76 6074 36,00 $1,00 63.33 6000
44.00
$4.15
14.9 72.01
60.35
11.30
23.59
66.90
58.31
79.99
75.55
11.23
54.0
01.42
70,99
12.39
$2.44
6665
51.11
53.00
05.00
36.85
06.75
72.94
17.55
78.40
66.22
79.63
74.22
8055
76.55
75/05
59.90
79.31
5000
75:5
62.99
Eacreta
68.99 6400
13.72
72.10
77.50
66.90
14.79
72.15
08.37
12.04
55.50
63.41
20.85
66.22
55.00
02.92
01.33
61.6
36.53 6000
13.50
46.15
70.1 6003
ReAn
57.86 57.0
67.74
09.59
14.05
03.03
02.00
16.71
54.53
59:67
53.51
51.81
53.64
76.00
75.33
0.31
4.88
63.06 60.06
LerinaNNol
51.11
59x0
62.45
6551
47.52
51.35
50.87
53.33
06667
51.49 61.57
55.35
Showi
Ve
1044
57.34
61.35
99 75:30
05.50
50.00
16:47
71
07:00 74.00
19.25
58.15
66.33
54.36
58.86
61.38
56.19
65.63
66:67
4.88
11:31
71.64 68.96
1ighOke
305g
52.54 46,42
64.70 51.59
61.00
52.00
13
54.53
71.40 50:50
54.00
45.75
05.92
48.37
52.22
42.93
5100
5066
43.39
45.39
2.5
10.86
53.13 56,00
70557
59.66
70:00
71.63
13.25
70.44 65:96 67.64
63.78
56.67
Jann 71-71
Ommial
D10k:
65.38 61.00
51500
22.19
14.50
75.00
13.60
20.51 76.77 71.0
60.77
56.00
00.71
66-45
53.01
55.3
61.16
00.34
13.33 To.00
78-16
02.08
74.66 73.51
R1
07.51 08.31
81:16
01:00
78 53.00
15 29:41 00:37
06.32 10 08.99
6118 91.00 63.93
17
0625 70.55
Midicerayy
Midyena
hoce
65:22 62.40
13.66 34.0
15.00
00.00
19.24
75.51
0612 0.:5
13:14
02.59
20.50
71.03
04-13
99.56
61.31
66.67
10.33
13.67
34.34
7932 75.32
S
Nicaal 20290
42.90 60.87
15.30
34.4
13.00
11.00
17.22 14.00
75,09
18.25
60.38
05.12
63.47
67.44
53.00
7031
66.15
6000 00.00
55.33
14.0
00.85
75.00 7465
BADEL
2005
77.55
IRD Clani
76.90
7A-7
83.17
81.28
76.90
02.00 10.54 15.25
74.6
75.82
17.25 73.54 72.00 10.00 T10o
72.76 76.03
54.11
(5.30 96355 82.1
8667
58.06
Table 6: GenAl-Bench Evaluation Results. means we report the reproducing results using the official Github repository and checkpoint. The best results are bolded.
Busic Pronut
Advanced Prosupt
Modd
Attributte
Scauet
Rdlation
Arg!
Count:
Diffce?
Compare?
Logical
Arx"
Overall
Spatial
-Ncoor
Part
Negszet
Uaivessl?
S8--1
0154
0.54
03817
0.83
0188
1183
01:3
11:6
0.50
11:60
0.6
0.75
FLUX
0.57
0.88
0.97
0.85
0.57
0.57
0.55
0.78
0.74
0.45
01.00
0.64
0.76
1WM
0.61
0.62
8.65
0.6%
11:0
1863
0.59
03:4
BS54
0.49
11:0
0.11
0.58
Sare
0.73
032
0.20
0:30
0.75
0.70
0.62
8711
0.51
0.60
0.85
VILA
377
0.77
0.7
1.70
0,70
0.74
065
UmiTa
0.76
0.76
0.70
0.46
0.75
0.67
Mogao-78 76
2. 2014
0.77
11.44
0.77
0.53
1171
0.6
Janan-P Pro
0135
0.87
033
0.84
085
1154
0.75
1.73
1.71
0.48
1105
065
0.75
SACEL-
390
0.89
0.88
0.59
0.70
0.68
Show-00
1155
0.64
0.75
BAGEL. nn/ adf-Cal"
Dong al 2005
0.86
0.58
0.87
0.87
0.82
11.66
0.81
0.78
0.81
0.66
0.77
0.25
0.81
(RG
0.900
0:92
0.91
0.90
0.500
1090
0.54
0.78
033
0.66
1030
0.77
0.54
We use Qwen2.5VL (Bai et al.) 2025b) to generate the initial thinking and improving thinking processes, while GPT-4o generates high-quality images.
Training settings. We adopt the unified multimodal understanding and generation model BAGEL (Deng al.) 2025) our base model. In the first training stage, we train the base model for 2K steps on the six decomposed learning modes using the cross-entropy (CE) loss and mean squared error (MSE) loss. We then continue training the model for 30K steps on the Initial Full Learning and Improving Full Learning tasks.
C DETAILED MAIN RESULTS ANALYSIS
To thoroughly assess the visual generation performance of our model, we conduct evaluation on series of representative benchmarks that cover complementary aspects of controllable and knowledge- grounded generation. Together, these benchmarks provide comprehensive view of our model's strengths in alignment, reasoning, stylistic control, and text rendering. In the following, we present detailed comparisons with SoTA baselines and highlight the improvements achieved by our proposed IRG model.
GenEval. Tab. reports the quantitative results on the GenEval Ghosh et al. 2023) bench- mark, which evaluates compositional T2I generation across diverse object-centric attributes such as counting, color, and spatial position. We benchmark both generation-only models and unified understanding-generaa models. Among generation-only approaches, FLUX. 1-dv achieves the
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Table 7: Quantitative evaluation results on OneIG-EN. The overall score is the average of the five dimensions. The best results are bolded and the second best results are underlined.
Model
Alignment
Text
Reasoning
Style
Diversity
Overall
Janus-Pro
Chen et al. C
2025c
0.553
0.001
0.139
0.276
0.365
0.267
BLIP3-o
hen cl al. 2025a
0.711
0.013
0.223
0.361
0.229
0.307
BAGEL
Deng ct a. 2025
0.769
0.244
0.173
0.367
0.251
0.361
Show-o2. [Xré et al 2025b
0.817
0.002
0.226
0.317
0.177
0,308
SDv1.5 [Rombach et 2022
0.565
0.010
0.207
0.383
0.429
0.319
SDXL [Podell et al
2024
0.688
0.029
0.237
0.332
0.296
0.316
FLUX.. -eev (Labs
2024
0.786
0.523
0,253
0.368
0.238
0.434
SANA- 1,5 4.8B (PAG) Xie et al
2025a
0.765
0.069
0,217
0.401
0.216
0.334
Lumina-Image 2.0 (Qin et al. 2025
0.819
0.106
0.270
0.354
0.216
0,353
BAGEL w/ self-CoT (eng et al.
2025
0.793
0.020
0.206
0.390
0.209
0.324
IRG (Ours)
0.839
0.372
0.239
0.427
0.192
0.415
GPT-4o (OpenAl 2025c
0.851
0.857
0.345
0.462
0.151
0.533
best performance with an overall score of 0.82. Within the unified category, our proposed IRG model achieves the best overall score of 0.85, consistently outperforming all baselines across multiple sub-tasks, including challenging aspects such as counting (0.83) and position (0.74). These results establish IRG
state of the art GenEval, demonstrating strong controllability and precise compositional generation capabilities.
WISE. Tab. reports the quantitative results on the WISE (Niu et al. 2025) benchmark, which evaluates T21 models on complex semantic understanding and world knowledge reasoning across six domains: culture, time, space, biology, physics, and chemistry. Unlike compositional tests such GenEval, WISE focuses on knowledge-grounee grounded generation where models must accurately reflect real-world semantics in addition to compositional control. Among generation-only models, FLUX.1-dev achieves the best performance with an overall score 0.50. Within the unified category. our proposed IRG model establishes new state of the art with an overall score of 0.77. It consistently outperforms prior unified models across all six domains, achieving 0.78 on cultural knowledge, 0.72 on temporal reasoning, 0.76 on spatial understanding. and above 0.80 in biology and physics. These results indicate that IRG not only improves general controllability but also integrates world knowledge more effectively than existing approaches, setting new benchmark for semantic alignment in T2I generation.
THE Tab. presents the quantitative results on THIF testmini (Wei et al. 2025), a benchmark specifically designed evaluate T21 models' ability to interpret and accurately follow complex natural language instructions. The benchmark covers both basic following (attributes, relations, and reasoning) and advanced following (multi-attribute reasoning, compositional control, stylistic adherence, and textual rendering), along with a separate evaluation of designer-oriented prompts. IRG model achieves the best overall
When evaluated with Qwen VL2.5-72B as the reference, performance with scores of 76.00 (short) and 73.77 (long). Notably. IRG demonstrates consistent improvements in advanced following tasks, achieving 75.25/74.66 on average, and excelling in compositional cases such as atrribute+reasoning 178.16/77.76) and relation+reasoningg .7...4.......
Overall, these results highlight the superior instruction-following ability of IRG, which consistently generalizes across different evaluation settings and significantly outperforms prior open-source systems, establishing as a new state of the art in controllable T2I generation.
GenAI-Bench. Tab. 6 reports results on GenAL-Bench (Li et al. 2024a). which probes composi- tional text-to-visual generation across Basic (Attribute, Scene, and Relation: Spatial/Action/PPa and Advanced (Count, Differ, Compare, Logical: Negate/Universal) prompts. Our model IRG attains the best Overall score 0.84, exceeding strong baselines. IRG delivers the strongest overall performance on RenAl-Bench. combining robust basic grounding (attributes, scenes, relations) with improved compositional and logical generalization on advanced prompts.
OneIG-EN. Tab. summarizes the quantitative results on OnelG-EN (Chang et al. 2025), the English track of the OnelG-Bench benchmark that evaluates fine- -re-grraned T2I generation along five dimensions: alignment, text rendering, reasoning, style, and diversity. The final overall score is
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Prompt: sacred symbol used in Jainism, representing the path to liberation and enlightenment
reasoning step (w/ naive CF6)
reasoning step 1
reasoning step 2 (w/o CFG text cache)
reasoning step 2
IRG (Ours)
(w/o CFG image cache)
Figure 4: The results of the CFG strategy visualization comparison. "w/ naive CFG" means using the same CFG strategy as the image generation process of reasoning step (i.e., applying traditional prompt-only CFG at reasoning step 2). "w/o CFG text cache" and "w/o CFG image cache" mean that the tailored CFG text cache and CFG image eache are not used during the improved image generation, respectively. Our proposed IRG uses dedicated CFG condition design for the improved image generation.
Table 8: Early stop comparison of IRG. The base method is BAGEL w/ self-CoT. "IRG w/ carly stop" means that we employ the MLLM model judge whether the IRG reasoning process can be terminated early given the prompt (see Sec. D.5).
Model
Early stop rate
WISE
IRG reasoning step1 IRG (Ours) IRG w/ early stop
0.79
0.77
71%
0.79
computed as the average across these dimensions. Our IRG model establishes new state of the art among open-source approaches with an overall score of (0.415), ranking second only to GPT-4o (0.533). IRG achieves the best alignment (0.839) and style (0.427) scores, and maintains balanced performance across reasoning (0.239) and text rendering (0.377). These results indicate that IRG not only excels faithfully aligning with user prompts but also produces aesthetically consistent outputs, demonstrating its superior general-purpont generation capability.
D MORE DETAILED EXPERIMENTAL RESULTS ANALYSIS
D. VISUALIZATION COMPARISON RESULTS.
As shown in Fig our proposed IRG demonstrates the strong ability to generate the images with high visual quality, fine-grat mrn-ggained details, and aesthetic aspects. As illustrated in Fig. ??, compared to BAGEL and BAGEL w/ self-CoT, our proposed IRG achieves superior generation quality and visual fidelity. Moreover, relative to the first-turn generated images, the reflection step in IRG improves the visual quality and fine- ane-grained fidelity of the initially generated images. For example, enhancing suboptimal textures and refining details that previously poorly rendered. This demonstrates that IRG not only produces images that are semantically correct but also places strong emphasis on fine-detail quality in the generated content.
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Prompt:
Honeybee.
IRG reasoning step1
IRG reasoning step2
WISE score: "consistency": 2.0
WISE score:
"consistency", 2.0
"realism": 2.0
"realism": 2.0
"aesthetic__uality"" 2.0
"aesthetii__qualit""" 2.0
MLLM judge: Win: 40 (1)
MLLM judge: Win: Qwen, UnifiedReward (2)
Lose: Qwen, UnifiedReward (2)
Lose: GP (1)
MLLM judge:
MLLM judge:
Lose: Human judger 1-3 (3)
Win: Human judger 1-3 (3)
Prompt:
A maple tree with fresh green leaves just emerging fully green leaves spring.
IRG reasoning step1
IR& reasoning step2
WISE score: "consistency"; 2.0,
WISE score:
"consistency" : 2.0,
"realism": 1.0,
"realism": 1.0,
"aesthetic_qualit"" quality:: 1.0
"aesthetic questhetic_quality"" quality": 1.0
MLLM judge: Lose: Qwen, GP,-40,
MLLM judge:
Win: Qwen, G4,-40,
UnifiedReward (3)
UnifiedReward (3)
MLLM judge:
MLLM judge:
Lose: Human judger 1-3 (3)
Win: Human judger 1-3 (3)
Figure 5: Visualization of the misalignment between benchmark scores and image quality. In this case, IRG reasoning step 2 correctly improves the leaf colors compared to step (where trees had incorrect colors), yet the WISE benchmark score remains identical. This illustrates that standard benchmarks may fail to capture fine-grained visual improvements that are aligned with human and MILM preferences.
D.2 CFG STRATEGY.
In Fig. compare the different CFG strategies in situations that affect improved image generation quality. It is clear that the dedicated CFG condition strategy combining the CFG text condition (conditioning with versus without the initial image information) and the CFG image condition
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Prompt:
An artful arrangement of white buttons outlines shirt design light- fabric, with controsting collar, potentially conveying an artistic interpretation shirt's concept.
Original image
Qwen Owen-image--i image-edit edit
Hunyuan image -.0
GPT-4o (ours)
Figure 6: Qualitative comparison of dataset construction using different foundation models. We com- pare the refinement capabilities of the open-source Hunyuan-Image-3. against GPT-4o. The results indicate that open-source models can effectively perform the reasoning-drit image improvement steps, validating the feasibility of reproducing the dataset construction using open-source resources.
Table 9: Results of extension to reasoning steps.
Model
WISE
TIIF
GenAI-Bench
IRG reasoning step
0.79
75.84/73.90
0.84
IRG reasoning step 2 (Ours)
0.77
76.00/73.77
0.84
IRG reasoning step
0.76
74.23/72.96
0.83
IRG reasoning step
0.75
74.41/72.81
0.83
(conditioning with versus without the reflection text) achieves the best visual quality and stability of generated images.
D.3 RELATION BENCHMARK SCORE AND IMAGE QUALITY.
We conducted further analysis, and the results demonstrate that all three MLLMs and three human annotators consistently prefer images generated IRG reasoning step 2 over step Notably, in the case shown in Fig. 5 IRG reasoning step 1 generated trees with incorrect leaf colors, which were correctly improved in step 2, yet the WISE benchmark scores remained identical. This phenomenon confirms that benchmark scores do not necessarily reflect image quality. The misalignment between text-to-image generation benchmark scores and actual image quality has been persistent challenge in the field, which the community continues to address. We recognize that standard benchmarks are often insensitive to fine-grained visual improvements and supplement them with multi-MLLM evaluator comparisons, providing convincing qualitative and quantitative validation.
D.4 OPEN-SOURCE MODEL FOR DATASET CONDUCTION.
We further investigate the feasibility of utilizing open-source models for constructing the IRGL dataset to ensure accessibility and reproducibility. Fig. о presents a comparative analysis of cases generated using the open-source model Mannuan--mmgge CCo et al. 2025) versus the proprietary GPT-4o. The results demonstrate that the open-source model is equally capable of reasoning and achieving significant, semantically correct improvements the initial generation. This empirical
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Table 10:
More detailed ablation study of IRG. "- Understanding Learning in Stagel Training" means that we convert all Understanding Learning data to Generation Learning and performing Stage training with the same number of steps. "- Full Learning in Stagel Training" means that we conduct an ablation study removing image learning (Initial Full Learning and Improving Full Learning) during Stage I training.
WISE
THF
GenAI-Bench
Model
Score
Rank
Score
Score
Qwen
GPT-4o
UniliedReward
Avg.
IRG (Ours)
0.77
5356
51%
51%
51.7%
76.01V73.77
0.84
Understanding Learning in Sugee Trinning
0.77
47%
49%-
49%
48.3%
75. 75.34/73.51
0.84
Full Learning in Stugel Truining
0.56
55.82/54.31
0.72
Prompt: Coost redwood tree, highlighting its towering height dense foliage.
IRG reasoning step1
IRG reasoning step2
MLLM judge: The generated image in reasoning step 1 is good enough.
Prompt: maple tree with fresh green leaves just emerging or fully green leaves in spring.
IRG reasoning step1
IRG reasoning step2
MLLM judge: The generated image reasoning step is not good.
Figure 7: Visualization of the early stopping phenomenon. The figure illustrates a scenario where the reasoning process terminated at Step 0, despite the subsequent Step (shown below) demonstrating clear improvements in detail and alignment. This highlights a limitation where the stopping criterion rejects valid refinements.
evidence supports the viability of our data construction pipeline using open- open-weight models, indicating that the proposed method is not dependent on specific proprietary APIs.
D.5 EARLY STOP.
Regarding whether generation can be completed at the first stage, we conducted an experimental study on the WISE benchmark. We employed the Qwen3-VL (Bai et al. (2225)) model to judge whether the IRG reasoning process can be terminated early, given the prompt. The results are presented in Tab. g introducing an MLLM to judge early termination is feasible (at least without degrading benchmark metrics). However, this introduces fundamental issue: the MLLM lacks reference information, making it difficult to determine whether subsequent IRG steps will yield further improvements, potentially leading erroneous judgments.
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Prompt:
AIRE CHARA
MICHAEL PHIEEN
Michael Phieen swimming.
IRG reasoning step1
IRG reasoning step2
Prompt:
The Great Sphinx of Giza with a lion's body and human head, surrounded by desert.
IRG reasoning step1
IRG reasoning step2
Prompt:
boy skateboarding the Great Wall.
IRG reasoning step1
IRG reasoning step2
Figure 8: Qualitative analysis of failure cases. This figure illustrates scenarios where the reasoning and refinement process yields suboptimal results. Green boxes denote areas where local improvements successful, while red boxes indicate regions where the intended corrections failed or introduced new artifacts. These cases highlight the challenges of subjective preference and complex constraint satisfaction.
Table 11: Inference cost of IRG.
IRG reasoning step
IRG reasoning step 2
Inference latency
29.79s
60.58s
Peak GPU memory
28.23GB
29.35GB
As shown in Fig. 7 We observe instances where the MLLM-basedd discriminator triggers early stop, terminating the inference process prematurely. In these cases, the discriminator halts the reasoning loop even when a subsequent refinement step yields result with superior visual quality better prompt alignment than the retained image. This phenomenon suggests that while the current stopping policy effectively prevents degradation, it can occasionally be overly conservative. Enhancing the discriminator's 's ability to recognize subtle but meaningful improvements remains promising avenue for future research.
D.6 ERROR ANALYSIS AND FAILURE MODES.
Visualization results (Fig. J and Fig. ??) reveal remaining failure patterns: (1) Micro-structuut saturation repetitive textures (e.g., fabrics, foliage), where the improving step occasionally over-
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ST
Prompt:
The priestly blessing hand gesture common in Judaism, with fingers spread to form the letter Shin.
IRG reasoning step1
IRG reasoning step2
IRG reasoning step3
IRG reasoning step4
Figure 9: Visualization of extended reasoning steps. The figure displays the progression of generated images across additional iterations (c.g., Step 3 and Step 4). While the model continues to refine the image, the marginal gains decrease after the initial steps, validating the effectiveness of the proposed stopping criteria.
smooths high fighufrequency details; (2) Text rendering drift under dense constraints, where the refinement trades legibility for stylistic coherence; (3) Global-local tension in crowded scenes, where local edits improve parts while slightly perturbing global layout. We find these most pronounced when T introduces many simultaneous edits; a conservative editing policy improves stability but may cap the attainable gains.
We further present a detailed breakdown failure instances in Fig. these scenarios, the model attempts address defects identified in the initial generation (Step 1). While certain regions successfully rectified (highlighted in green boxes), other intended improvements fail to materialize or executed incorrectly (highlighted in red boxes). Additionally, observe instances where the refinement is inherently subjective; in such cases, human evaluators may prefer the initial generation despite the semantic adjustments made during the reasoning steps. Notwithstanding these specific failure modes, our quantitative evaluation confirms that the proposed method yields improvements aligned with human preference the majority of cases.
D.7 EXTENDING TO MORE STEPS REASONING.
We further investigate the behavior of our framework when extending the inference process to additional reasoning steps beyond the default configuration. As presented in Tab. P the results show that extending more rounds leads to the fact that IRG is trained only on two-round data. Additionally, since IRG already substantially improves image quality by the second round, subsequent improvements are relatively minor, and potential error accumulation may lead slight performance degradation.
slight decrease IRG's benchmark scores. We attribute this
As shown in Fig. 9 the model remains capable of performing valid reasoning and modification in subsequent iterations. However, empirical observations suggest that the most significant quality improvements are typically achieved within the initial refinement steps. Extending the process further tends yield diminishing returns regarding prompt alignment and visual fidelity, and in some instances, may lead to over-editing This analysis supports the efficiency of our dynamic stopping mechanism, which balances generation quality with computational cost.
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D.8 MORE DETAILED ABLATION STUDY AND ANALYSIS.
For the more motivation analysis of Decomposed Learning Modes, Both Understanding Learning and Generation Learning are designed enable the model to learn text-based reasoning capabilities, with key distinctions in their approaches. Understanding Learning directly learns text-based reasoning grounded in visual information. For instance. Initial Thinking Understanding Learning learns to generate initial thinking given an image, while Improving Thinking Understanding Learning learns to produce improving thinking given both first and second round images. contrast, Generation Learning is designed to directly simulate the actual IRG reasoning process. For example, Initial Thinking Generation Learning mimics generating initial thinking directly from the given prompt, while Improving Thinking Generation Learning generates improving thinking based on the previously generated image and reasoning chain. This design motivated by the intuition and empirical observation that learning thinking with visual reference versus learning thinking directly from text poses different learning challenges for the model-the former benefits from contrastive visual references, while the latter requires reasoning with more limited information. Additionally, during Stage training, we observed that these two training approaches exhibit different convergence characteristics.
As shown in Tab. 10. The results demonstrate that employing both Understanding Learning and Gen- eration Learning during Stage versus using only Generation Learning achieves similar benchmark scores, However, in MLLM-based image quality judgments, IRG slightly outperforms the variant without Understanding Learning. For the ablation of whether removing image learning during Stage I training, this ablation study explicitly demonstrates the necessity of incorporating full thinking-image trajectories during Stage training.
D.9 INFERENCE COST OF IRG.
As shown in Tab. II the two-step reasoning process of IRG does introduce additional inference overhead. However, this overhead primarily manifests in inference latency rather than peak GPU memory consumption (where the majority of GPU memory occupied by model parameter loading). In this scenario, deploying this model for interleaved reasoning generation in practical applications is generally not constrained, as it does not require substantially additional GPU memory that would limit device deployment. The primary impact is on generation latency, which aligns with the bottleneck observed in LLM reasoning models.
Furthermore, we would like to clarify that this additional overhead is an inherent characteristic of test-time scaling techniques. When we aim to enhance model performance through inference-time techniques, such overhead is expected and unavoidable compared to non-reasoning models, and is not a limitation unique to our approach. Our proposed IRG introduces both text-based and visual-based reasoning into the text-to-image generation domain, representing an innovative contribution to this field and providing powerful new paradigm for further enhancing generative model capabilities.
D.10 FULL CASE VISUALIZATION.
We provide comprehensive visualizations of the full inference process in Fig. 10 and II These figures display the intermediate reasoning outputs, including the generated images at each step, the corresponding critique and reasoning traces provided by the MLLM, and the refined prompts used for subsequent generation. This transparent view of the interleaving process highlights how the model iteratively identifies specific such as spatial inconsistencies or missing details corrects them to align better with the user's intent.
E LLM USAGE
In this paper, we have used an LLM to refine some sentences and improve the grammar, making the paper more academic.
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Prompt: A fierce pirate wearing a skull triangle hat is standing on the beach
<thinko
The model should generete an image of pirete with dull sarring standing tresare idand, copturing regped and odverturece otmosphere vibrant
dyronic lighting highlghtt the pleate's
feotures
the
behind lin.
rugged pirote stands promingetly the foreground, his long, tousled hair and thick board froning weathered face narked by years of seafering adiestures, He wears clask pirate outfit consisting of a micor het adorned with a stall and creasiones, e breen vest over a striped shirt, and belt with large buckle His right hard nests confidertly on his hi, while his left hard gripa a weoden staff, eynboliznng his autrerity and strength. distinetive shall earring adds touch of mexoce and indiviuudity to appearance
Hore's the full, detailed
The pirate stards of goldon sand, the backigrund, majastic phote ship with multiple share, its sols catching the wind. The ship detailed with rapea, riasing, and flago, adding the authenteity of the scene. snall rowboot rests on the condy seach, neady for use.
everlooking lish,
treasure island dotted with
and stretchea and billowing colls anchored sear
The shy above vibrant blue, filled with fleffy white cloud that cast soft shadows over the landscape, creating sense depth and dimension The lighting bright natural, highighring the tectures of the pirate's clattingg excitement and advanture, with hint of doninating the foreground and cooler blues and greens the bockground. enhancing the controst and viaua appeal of the scene. The compottion is balonced. with the pirtee as the centrol focus, drowing the wewer's towarde him and then, outwards to the treasure and the ship, oreating sarvative of expleration and discovery. ./thnks
overall with The color polette nich and
<think
The pravioss genarated image succesefully captures essence pirate on treasure ieland but falls short severel areas. The pirane's expression appears soncehst acutral nather conveying the rugped determination typical pirgte, Additionally background atmosphere. lighting color could also which detroets dynamic and imnereive
IRG reasoning step 1
pirate detail
the
enhanced create
##e bataile Explonation of Required Inprovenents:
The pirate's expression needs more interas and commanding. reflecting his rale as feorless leader. This schloved by adding o dight frown deoponing the lines
visille rigsing, spiling flags eyes mouth. The pirate alding
should shoul
its of the
prominast and detailed, with gold dramatic effect, with shadows and highiighs emphesiaing the textures
The
lighting should adjusted to create of the pinste's ' clething and the survourding anireoment. The color polette should richer and more vibrant, with deeper blues and greens in the sky and sea, and worner tones in the foreground.
#o8 Step-by- Step Modifisation Guidonce:
1. Adjust the pirate's facid expression to
intence and commanding with dightt frow and despened lines
cround his and mouth.
2. Enhance the details of the pinate ship in the background, includin rigging. salle, and floge, to make it more realatic.
S. Make treosure chast more prominent and detailed with gold coie spiling out, adding to the scens's is allune. a more dramatic effect, using shadows and hightights amphosize textures of Modify lighting the surrouddng enironment.
the day and and Adjust color palette richer and virrant, with deaper blues and in the foragroand.
#BR Finel Comprehanaive Frompt for the Improved Image:
A rugeed pirate standa praminantly in foreground, hix long, toualed hair and thick beard franing a determined marked years seaforing adventures. He weene and crosbbores,
pirtte outfin consiting buclle. Hi hard wooden staff. rymbaliing authority and strength. disticctive
adomed
confidantly he while his left hand shull corving odds a touch of nenace and individudiiy to his appearence.
The pirate stands on rocky sutcrop overlooking a luth, green treasure ieland dorted with pain trees and stratches of goldon sard. the bachground, najestic pirate ship with nuttiple meats billwing sal3 aschored sear soils cstching wind. ship
ropes. rigping, flags. adding the authenticity sandy beech,
snall rowboat
The sky above a wbbrntt blue, filled with fluffy white clouds that coct ooft shodoes over the landsoape, creating a sense of depth and dimension. The lighting bright and natural, hightighing the textures of the pirate's a catthnng the rocks benesth his feet. and the gleaning mossure chest filled with gold cons, The everall moad one of extitement advesture, with him danger mystery. The color palette is rich and warm, carthy tones dominating the foreground and composition balonced. with and greane the and then outwards the treasure and the creating sarrative exploration and discovery. </hhink
IRG reasoning step 2
background, enhoncing focus, drowing viewer's towards him
appecl of
Figure 10: Full case visualization (Part 1). This figure illustrates the complete reasoning chain for selected samples. It shows the input prompt, the initial image generation, the specific reasoning feedback (critique) generated by the model, and the resulting improved image. This demonstrates the model's 's ability to celf-correet based on its own reasoning.
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Prompt: Easter egg hunt
<thinko
The model should generate on image of traditional Easter backet filled with colorfal chocolate agpe, decorated with vibramt spring flowor bouquet and a yellow egg. set agaist a worm wooden beckground to evole festive and cheerful Easter stmosphere.
crange, wicker surfeccs reflecting light partern, sitting basids small fluffy white burry figurine. Beside the basket, deliccte spring flower bauouet conclsting of pink rulipo, white dosies, and yellow bloasoma adds a touch of freshness and vibrancy. The flowers are accompanied by lush green leaves, enhancing the natural and festive feel. The entire scene ost agoinat worm, smooth wooden ourface that provides o seft contrast bright colors
Here's full, denailed prompt: Croste on image of a classic Easter bosket, woven from
sturdy
charming yellow with blue their eggs
of the basket arronged seatly within
eggs flowers. the gloasy eggs, asseciated mediriond
lighting soft and netural ceating gestle shadows composttion festiwties, The
highlighting textures joy, polette bright and cheerful, with harmanious blend pastel and vivid hue that onhance the festive moad. <Phinko
petols. The
IRG reasoning step 1
cthink
traditional Easter basket folls short successfully coptures somewhat cluttered, and the flower bouquet lacks vibronsy and ceveral The arrangement of the voriety. Additionally the lighting scene flat, fallig fully highight the textures and colors of the objects. The overall compasition could benefit from a more balarced and vasoly appealing errnngement.
previous
betailed Explonation of Required Inprovementa:
the lighting. The should
need
the composition, enhancing the polette. and improving that
pleasing visual flow, doninatees the scene. The flower bouquet requies addinioed variety and depth, incorporeting differest types of flowers and leaves to odd texture and interest. The lighting should be adjusted to cast more dyramic shadows and higlights, emphosising the three dinessional quality of the objects. Lastly, the overal color balonce should be fine- tured to schiove a more harmanioue and vibrast lask.
delierately
color
### Step-by- Step Medifisstion Guidance:
1. "*Composition "djustmess" Reongonise the egga within the basket to create more baleneed and aesthatically ploosing arrongemant Ensure that the egge are not ovencronded and that there is enough space for the flowors and the buney figurine. Roce the eggs in way that cloos their colors to complement each other naturally.
"*Flower Bouquet Enhascenent Introduce Posttion the flowers strotegically forget-ma-net
variety flowers and leaves bouquet. Add more basket frane the green foliage the effectively.
depth.
3. **Lghtinng Improveeett": Adjust the lighting to create more dynanic and natural effect. Use soft, diffused fight asurce to cast gentle shadows and highlights on the eggs. flowers, and baaket. This will help to bring out the textures and colors more viidly and give the scene more lifelke appearance.
oahiave harmonious vibrant Ensure others. Make the colors bright and true life, contributing to the festive and cheerful atmosphere.
"CColor shat
"e tune
the
basket blend
without
##M Finel Comprehansive Prompt for the Improved Image:
Greate an image of a classic Easter basket, woven from brown wicker atundy handle, brimming with on eggs
yellow, orange, pink, overcroaded purple. Amonge space for the
balanced and aesthetically pleasing flowers and the bunny figurins. Adorn the of the basket with charming yellow featuring blue zigin pottern and small, fluffy white burny fgurine, Beside the bosket, place a wbrent and voried flower bouquet consisting of pirk. hulips, white doises, yellw blossome, and kus green lcoves. Set the acene apainst warm, smooth woodes surface that provides soft controst to the bright colore. Use saft, diffused lighting cust gestle shadows and highlights, emphesiting the textures and colors of objects. Fine. tune color balance achicve balanced ithinks
IRG reasoning step 2
look, ensuning joy,
bright celctration asseciated
overal compostton Easter festivtties.
Figure 11: Full case visualization (Part II.. Additional examples of the iterative refinement process. The visualization highlights how the interleaving reasoning mechanism effectively handles complex prompts by breaking down the generation into manageable refinement steps, ensuring high fidelity to the text description.
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Reviews:
[{"review_id": "UXt4UnEWu7", "reviewer": "Reviewer_JwV8", "summary": "The paper introduced Interleaving Reasoning Generation (IRG). The main idea is to reason with both text and image during text-to-image, to produce better images in the end. Specifically, the process in this paper has 4 stages: (1) Given an input text prompt, generate textural \"thinking\" (2) after thinking, generate an initial image (3) generate the second textural \"thinking\" to refine the image (4) generate the final image based on the previous steps.\n\nThe training process includes 6 learning modes, each targeting different sub-steps above. The authors also created IRGL-300K, a training dataset for this kind of interleaved reasoning generation. The primary source of the images are from GPT-4o. The authors also used Qwen2.5-VL to generate the text data.\n\nThe authors conduct experiment on Bagel, showing that it improves the results on GenEval, WISE, TIIF, and GenAI-Bench. The authors also conduct ablation studies on various components in the training process.", "questions": "Most of my questions have been discussed in the weakness section\n\n1. Can this method work without using GPT-4o distilled data? For example, using Qwen-Image. How will the performance change.\n\n2. Is it possible that with Bagel's self-generated data, and some real images, we can see such gains? \n\n3. More analysis on the reasoning steps would be very helpful. Like more analysis on the reasoning lengths, and some failure case examples.\n\n4. What is the inference cost of the method? Is it possible that in some cases, we can just finish generation in the first stage, to save some cost.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The idea of let unified models to use interleaved text and image reasoning steps for text-to-image generation makes a lot of sense. It utilizes unified models' power, and goes beyond bagel's COT + image generation scheme.\n\n2. Experiments show that the authors' approach works, and can give improvement compared to the original Bagel.\n\n3. The authors conduct experiments on a variety of tasks, and the ablations answer many important research questions.", "weaknesses": "1. The method is heavily dependent on distilling GPT-4o images. The dataset for \"initial learning stage\" is adapted from a GPT-4o generated dataset. The second stage is created through GPT-4o and Bagel generated images. It remains an open question that if this method works without a strong text-to-image model to distill with. For example, if the authors can observe similar gains through data generated by another open text-to-image model? Or even from Bagel's own generations? That would make the paper much stronger.\n\n2. There is no human evaluation in this paper. All evaluations are model based, and some are MLLM-as-a-judge results. It would be benefitial to show some human results on a subset of the testing prompts, to confirm that this method indeed improves model performance.\n\n3. There is no discussion on the compute / inference cost of this method. With more stages and many CFG steps, there will be a lot of extra compute, and they worth discussion.\n\n4. It would be beneficial to show some more analysis on the final model. For example, what is the lengths of the thinking steps? Are there any failure examples that the second image is not improving the first image?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hqlrD12MoQ", "reviewer": "Reviewer_5XBw", "summary": "This paper proposes Interleaving Reasoning Generation (IRG), a framework that improve T2I generaton by interleaving reasoning.\n\n\nThe key idea is to let the model first \"think\" (generate textual reasoning), then generate an image conditioned on that reasoning, and finally \"reflect\" on the generated image to refine it. \nThe framework differs from previous \"multi-turn post-hoc self-refinement / editing\" methods, IRG integrates reasoning and refinement in a single end-to-end pipeline via the supervision of \"text–thinking-image–thinking–image\" trajectory.\nTo support this paradigm, this paper introduces IRGL-300K, a large-scale dataset with six decomposed learning modes designed to train both text-based reasoning and full image generation capabilities.\n\n\nComprehensive experiments demonstrate strong improvements (5–10 points) across multiple benchmarks (GenEval, WISE, TIIF, GenAI-Bench, OneIG-EN).\n\n\nThe approach is conceptually interesting, technically solid, and empirically effective.", "questions": "My questions focus on clarity and rationale. \n\n1. Lines 288–290 \n> The main goal of this stage is to strengthen the text-based reasoning capability, while incorporating full thinking–image trajectories to avoid degrading the core generative performance.\n\nIt is unclear why \"incorporating full thinking–image trajectories\" is necessary during the reasoning-focused stage.\nWas this motivated by empirical observation (e.g., observed degradation of image generation without such data)?\nOr is it based on the general expectation that further fine-tuning of MLLMs may cause forgetting of generative capabilities?\n\n2. Lines 199–200\n> The model exchanges and exploits multiple segments of interleaved text–image representations, a process we term Interleaving Reasoning Generation (IRG).\n\nThe meaning of \"exchanges and exploits multiple segments of interleaved text–image representations\" is not clear.\nDoes this refer to multi-modal token interleaving within the transformer architecture,\nor to multi-step reasoning across modalities (e.g., text → image → text → image)? \nThis statement could be misleading and might need clarification.\n\n3. Section 2.2.2. \n\nThis section is quite dense and terminology-heavy.\nIt would be valuable to explain the thought process that led to the definition of the six decomposed learning modes.\nWas this decomposition empirically derived or conceptually motivated?\nA clear justification would significantly improve readability and insight.\n\nAdditionally, according to the mapping pattern (Eq. 4 →Eq.  7, Eq. 5 → Eq. 8, Eq. 6 → Eq. 9),\nwhy does Eq. 7 not include T^{(1)}_{out} as an input, while Eq. 8 and Eq. 9 do?\nThis asymmetry deserves clarification.\n\n4. Section 2.2.4. \n\nYou propose two complementary CFG-conditioning schemes (image-based and reflection-based).\nWhy not also include the traditional prompt-only CFG (conditioning on T_{in}) as a baseline?\nWas this excluded for theoretical or empirical reasons (e.g., redundancy, instability, or negligible impact)?\nDiscussing this design choice would improve completeness.\n\n5. Figure 2 is difficult to understand.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. The proposed Interleaving Reasoning Generation (IRG), which differs from previous  \"multi-turn post-hoc self-refinement / editing\" methods. Instead of iterative post-hoc image editing, IRG integrates reasoning and refinement in a single end-to-end pipeline via \"text–thinking-image–thinking–image\" supervision. This unified formulation is both elegant and innovative.\n\n2. The approach is not limited to improving semantic alignment (as most prior reflection-based methods do) but also targets to improve visual quality, including texture rendering, fine-grained detail, and shadow realism.\n\n3. The IRG pipeline achieves consistent gains of 5–10 points on five diverse text-to-image benchmarks (GenEval, WISE, TIIF, GenAI-Bench, and OneIG-EN), demonstrating strong generalization and scalability.\n\n4. The authors recognize that standard benchmarks are often insensitive to fine-grained visual improvements and supplement them with multi-MLLM evaluator comparisons, providing convincing qualitative and quantitative validation.", "weaknesses": "Seen in Questions", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zxHTm2kxgc", "reviewer": "Reviewer_bEWK", "summary": "This paper proposes Interleaving Reasoning Generation (IRG), a framework that alternates between text-based thinking and image synthesis to improve T2I generation quality. The key idea is: (1) generate initial thinking and an initial image, (2) reflect on the initial image to identify improvements, and (3) generate a refined image. To train IRG, the authors propose IRGL-300K, a dataset with six decomposed learning modes covering text-based thinking and full thinking-image trajectories. A two-stage training pipeline is employed: Stage 1 builds reasoning and reflection capabilities across all six tasks, while Stage 2 fine-tunes the complete IRG pipeline. Experiments show improvements of 5-10 points on GenEval, WISE, TIIF, GenAI-Bench, and OneIG-EN benchmarks.", "questions": "The paper's core idea is intuitive, but the proposed solution is exceptionally heavyweight, involving significant costs for both data creation and inference. Given this high investment, the actual benefits need to be clearly demonstrated, but the evidence presented is unconvincing and contradictory. Specifically, the results in Table 4 are confusing, as automated benchmarks show little to no improvement from the second reasoning turn. Furthermore, in the qualitative examples (Figure 4), the refined image can lose important details. The central issue is whether the method's substantial costs are justified by a reliable and significant improvement in image quality. The current results fail to make a convincing case. I hope the authors can provide clarification on these critical points.\n\n**Training & Data Cost**: What were the resource costs (e.g., API expenses, GPU hours) to create the IRGL-300K dataset and train the model?\n\n**Inference Cost**: How much slower is the 2-turn generation process compared to a single turn? Can you provide concrete numbers for inference time and memory usage?\n\n**Evaluation Contradiction**: MLLM ranking prefers 2-turn generation, but benchmark scores in Table 4 do not. Have you conducted human evaluations to confirm that the second turn offers a genuine improvement and rule out potential MLLM bias?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**Intuitive Framework**: The paper's core contribution, the Interleaving Reasoning Generation (IRG) framework, is conceptually elegant and highly intuitive. The \"think-generate-reflect-refine\" process mirrors human creative strategies, making the approach easy to understand and appreciate. \n\n**Methodological Depth**: The creation of the IRGL-300K dataset and the design of the two-stage, six-mode training process demonstrate a deep, systematic approach to solving a challenging training problem.", "weaknesses": "The paper presents an interesting idea but is undermined by significant methodological flaws, an absence of critical analysis, and unconvincing experimental results. The core weaknesses fall into two main categories: the questionable contribution of the proposed method and the unaddressed practical costs.\n\n**Unjustified Complexity and Missing Cost-Benefit Analysis**\n\nThe primary weakness is the paper's failure to justify the substantial increase in complexity and cost. The Interleaving Reasoning Generation (IRG) framework introduces significant overhead at both training and inference stages, yet the benefits are not clearly demonstrated.\nCritically Absent Efficiency Analysis: The proposed 2-turn method involves a 5-step sequential pipeline (initial thought → initial image → encode → reflection → refined image). This inherently introduces significant latency compared to single-step generation. The paper completely omits any discussion of inference efficiency, including critical metrics like inference time, memory consumption, or throughput. Without this analysis, it is impossible to assess the practical viability of the method.\n\n\n**Questionable Marginal Utility**\n\nThe paper's own results cast doubt on the core value proposition of the second \"improving\" turn.\nEvidence of Negative Returns: According to Table 4, single-turn generation from the IRG-trained model outperforms the 2-turn generation on the WISE benchmark (0.79 vs 0.77). This suggests that the reflection and refinement step can actually degrade image quality. The paper fails to investigate or explain these failure cases, leaving a critical gap in understanding the method's behavior.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gA9kQzq4of", "reviewer": "Reviewer_PpTp", "summary": "The paper introduces a novel **Interleaving Reasoning Generation (IRG)** framework for text-to-image (T2I) generation. The key idea is to alternate between text-based reasoning and image synthesis, enabling the model to progressively refine image quality and semantics. To support this, the authors curate a **large-scale IRGL-300K dataset** comprising six decomposed learning modes designed to teach both textual reasoning and full image generation trajectories. Experimental results on **5** benchmarks, including **GenEval, WISE, TIIF, GenAI-Bench, and OneIG-EN**, show consistent improvements over existing T2I models or reasoning T2I approaches.", "questions": "1. Why can the proposed training pipeline validate the process? (weakness 1) \n2. Can the model generalize to a higher reasoning step with a fixed window size? (weakness 4)\n3. Given Eq. 4, what will the $T_q$ look like? \n4. What is the inference time comparison compared to other approaches? Since this approach requires extra reasoning, will this double the inference time? \n---\n **(suggestion)**: It can increase the readability to add a prefix in Eq. 1. For example, put \"image understanding\" for the first line and \"T2I with CoT\" for the second line.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 4, "strengths": "1. The paper is well-structured and clearly presented, with thoughtful visual aids such as color-coded pipeline diagrams (e.g., Fig. 2) that make complex processes easy to follow.  \n2. It addresses an **important and timely challenge** in multimodal generation to bridge reasoning and image synthesis, and provides not only a methodological contribution but also a valuable 300k-sample dataset, which could benefit future research.  \n3. The experimental validation is strong, demonstrating clear and consistent gains across multiple benchmarks, indicating that the proposed approach is both effective and generalizable.", "weaknesses": "1. **Lack of ablation on pipeline design rationale**. While the paper offers a detailed description of the six learning modes and two-stage training, it does not empirically justify whether each component is necessary. For example, it remains unclear if *Initial Thinking Understanding Learning* is crucial, or if training could skip directly to *Initial Thinking Generation Learning* since in Eq. 3, $T_{in}$ will not consider any visual input to generate $T\\_{out}^{(1)}$. An ablation isolating these stages would help clarify the necessity and impact of each design choice.  \n2. **Limited qualitative demonstrations**.  The paper would benefit from showing more examples of intermediate reasoning outputs (e.g., textual thoughts and intermediate images). Figure 4 showcases final results, but visualizing reasoning steps, similar to the teaser in Fig. 1, would provide clearer insight into how the reasoning process contributes to image refinement.  \n3. **Insufficient comparison to closely related works**. The paper overlooks recent open-source approaches with similar reasoning-generation mechanisms, such as *Self-Correcting with LLM* [1] and *CoT-based Image Generation* [2]. Including these in the comparison, even qualitatively or in terms of design philosophy, would strengthen the paper’s contextual positioning.\n4. **Limited exploration of higher reasoning steps**. The paper only discusses cases where $n \\le 2$. Extending the study to higher reasoning depths could further emphasize the strength of the proposed *interleaving* reasoning design. For instance, even if trained with $n=2$, the model could use a sliding window of size 2 to perform continuous reasoning, achieving $ n=3, 4$, or even $5$. This would provide deeper insights into how well the model generalizes to multi-step reasoning scenarios.\n\n---\n\n**Overall:**  \nThis is a promising and well-written paper tackling an important topic in T2I reasoning. The contributions are solid, but the lack of discussion and empirical validation of the training pipeline design slightly weakens the argument for its necessity. The current score is 4, which could be raised if the paper includes clearer ablations and more intermediate reasoning visualizations.\n\n---\n\n[1] Wu, T. H. et al. *Self-Correcting LLM-Controlled Diffusion Models.* CVPR 2024.  \n[2] Guo, Z. et al. *Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step.* CVPR 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)