Title: T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT
Venue: NeurIPS 2025
Accepted: yes
Keywords: Autoregressive Image Generation; Chain-of-Thought (CoT) Reasoning; Multimodal Large Language Model
Abstract: Recent advancements in large language models have demonstrated how chain-of-thought (CoT) and reinforcement learning (RL) can improve performance. However, applying such reasoning strategies to the visual generation domain remains largely unexplored. In this paper, we present **T2I-R1**, a novel reasoning-enhanced text-to-image generation model, powered by RL with a bi-level CoT reasoning process. Specifically, we identify two levels of CoT that can be utilized to enhance different stages of generation: (1) the semantic-level CoT for high-level planning of the prompt and (2) the token-level CoT for low-level pixel processing during patch-by-patch generation. To better coordinate these two levels of CoT, we introduce **BiCoT-GRPO** with an ensemble of generation rewards, which seamlessly optimizes both generated CoTs within the same training step. By applying our reasoning strategies to the baseline model, Janus-Pro, we achieve superior performance with 13% improvement on T2I-CompBench and 19% improvement on the WISE benchmark, even surpassing the state-of-the-art model FLUX.1. All the training code is in the supplementary material and will be made public.

T2I-R1:

Abstract

Recent advancements in large language models have demonstrated how chain-ofthought (CoT) and reinforcement learning (RL) can improve performance. However, applying such reasoning strategies to the visual generation domain remains largely unexplored. In this paper, we present T2I-R1 , a novel reasoning-enhanced text-to-image generation model, powered by RL with a bi-level CoT reasoning process. Specifically, we identify two levels of CoT that can be utilized to enhance different stages of generation: (1) the semantic-level CoT for high-level planning of the prompt and (2) the token-level CoT for low-level pixel processing during patch-by-patch generation. To better coordinate these two levels of CoT, we introduce BiCoT-GRPO with an ensemble of generation rewards, which seamlessly optimizes both generated CoTs within the same training step. By applying our reasoning strategies to the baseline model, Janus-Pro, we achieve superior performance with 13% improvement on T2I-CompBench and 19% improvement on the WISE benchmark, even surpassing the state-of-the-art model FLUX.1. All the training code and data are available at https://github.com/CaraJ7/T2I-R1 .

1 Introduction

The emergence of advanced Large Language Models (LLMs) [62, 64, 81, 95], such as OpenAI o1 [65] and DeepSeek-R1 [20], has demonstrated considerable reasoning capabilities across domains including mathematics [1, 27, 55, 54] and coding [8, 30, 53]. Through reinforcement learning (RL) [72, 73, 52], these models analyze problems progressively with a comprehensive Chain-ofThought (CoT) [84, 36, 24, 32, 105, 23] before providing answers, significantly enhancing output accuracy.

The CoT reasoning strategies have also been extended to the visual domain. Recent Large Multi-modal Models (LMMs) [7, 59, 103, 92] have adapted the paradigm to accommodate the visual understanding task [51, 105, 32, 22]. These advanced LMMs can jointly process images and their associated textual queries, performing step-by-step analyses of visual details and integrating them with reasoning steps to derive final answers. Concurrently, CoT-like reasoning has been initially investigated in the visual generation task [70, 99, 74, 37, 35, 10, 34, 100], particularly in autoregressive text-toimage generation. The pioneering work, 'Image Generation with CoT' [24], regards the progressive generation of the image tokens as a kind of CoT analogous to that of the text tokens, and proposes to optimize this intermediate process to enhance the image quality.

CoT in Image Understanding

A black cat with shiny

fur and bright green

eyes is sitting in front

of  a brown mouse with

tiny paws and a

twitching nose.

'A black cat and

a brown mouse.'

How does the whole

image look like?

ü

Global	Semantic

ü

High-level	Planning

The mouse is perched

on a small branch with

leaves sprouting from it,

and the background

appears to be a lush

garden with vibrant

flowers and foliage.

The cat gently gazes at

the mouse, while the

mouse playfully

scurries around the

branch.

How does the next

patch look like?

ü

Local	Details

ü

Low-level Processing

Reasoning Process

Reasoning Process

Semantic-level CoT

Token-level CoT

Question

As shown in the figure, AC is

the

diameter  of   circle

O,  if

angle OBC = 40 degrees, then

the degree of  angle AOB is

A: 40

°

B: 50

°

C: 80

°

D: 100

°

How to obtain the

final answer?

Reasoning Process

Step 1: In the circle, AC

is a diameter and

∠

OBC = 40

°

. Point O

is the center.

Step 2: Triangle OBC is

isosceles (OB and OC

are radii), so

∠

BOC =

180

°

- 40

°

- 40

°

= 100

°

.

Step 3: Since AC is a

diameter,

∠

AOB = 360

°

- 180

°

- 100

°

= 80

°

.

The answer is C.

CoT in Image Generation

Image Prompt

'A black cat and

a brown mouse.'

Answer: C

Despite these advances, the exploration of CoT for image generation remains preliminary. Unlike image understanding, image generation requires the complex interpretation of cross-modal alignment and the synthesis of fine-grained visual details. To address these challenges, we identify two distinct levels of CoT reasoning that can be leveraged to enhance image generation, as illustrated in Fig. 1:

Semantic-level CoT is the textual reasoning about the image to generate, which is introduced prior to the image generation. The semantic-level CoT designs the global structure of the image, e.g., the appearance and location of objects. In case the prompt requires reasoning shown in Fig. 2, the semantic-level CoT also helps to deduce the objects to generate. Optimizing the semantic-level CoT could explicitly decouple the planning and reasoning of the prompt from the subsequent image tokens generation, making the generation easier.

Token-level CoT is the intermediate patch-by-patch generation process of the image, as originally introduced in [24]. This process could be viewed as a form of CoT as it outputs each subsequent token conditioned on all previous tokens within a discrete space, similar to the textual CoT. Unlike semantic-level CoT, token-level CoT focuses on low-level details like pixel generation and maintaining visual coherence between adjacent patches. Optimizing the token-level CoT can enhance both the generation quality and the alignment between the prompt and the resulting images.

Despite recognizing these two levels of CoT, a critical question remains unaddressed: How can we enhance and coordinate them for text-to-image generation? Current mainstream generative models [76, 79, 70, 37] are trained exclusively on generation targets, lacking the explicit textual understanding required for semantic-level CoT reasoning. Although introducing a separate model (e.g., an LLM) specifically for prompt interpretation [13] is technically feasible, this approach would significantly increase computational costs, complexity, and deployment challenges. Recently, a trend has arisen to merge visual understanding and generation within a single model. Building upon LMMs, these unified LMMs (ULMs) [86, 93, 107, 9] could not only understand the visual inputs but also generate images from text prompts. However, their two capabilities are still decoupled, typically pre-trained in two independent stages, with no clear evidence that the understanding capabilities can

The	image	depicts	a	beautiful	and	intricate

igloo

...

The	igloo	has	an

aerodynamic	dome	shape

,	showcasing....

Its	surface	appears	smooth	and	translucent...

The	entrance	to	the	igloo	consists	of	a	gently	sloping	tunnel...

Janus-Pro

T2I-R1

A structure made of

ice, traditionally built

by the Inuit people

Underneath

the	train,	on	the	bottommost	part	of	the	platform,

lies

a

tiny	pig	with	a	brown-yellowish	coat.	The	pig	is

curled	up

,	resting

peacefully,	surrounded	by	the	iron	structure	of	the	train	tracks	and	the

dimly	lit	platform...

A	circular	emblem	depicting	the	balance	and	harmony	of	Taoist

philosophy	features	the

symbol	prominently	in	its

center.

The	emblem	showcases	a	harmonious	arrangement...

An image of  a circular

emblem representing

balance and harmony in

Taoist philosophy

a pig on the bottom of

a train

Prompt

Token-level CoT

Semantic-level CoT

The	image	portrays

tulips

,	the	famous	flower	that	is	grown	in	the

country	where	Amsterdam	is	located.

The	vibrant	colors	of	these	flowers,	ranging	from

yellow	and	red	to

purple

...

A specific type of

flower cultivated in

the country where

Amsterdam is located

O

司

benefit generation. Given these potentials and issues, we start from a ULM and enhance it to unite both the semantic-level and token-level CoT into one framework for text-to-image generation.

To fulfill our target, we introduce BiCoT-GRPO , an RL method to jointly optimize the two levels of CoT for ULM. We opt for RL instead of supervised fine-tuning (SFT) for two reasons: First, the ULM has possessed the fundamental ability needed for the semantic-level and token-level CoT; our goal is only to elicit the fusion of these two abilities by guiding the model's self-exploration. Second, RL methods have proven highly effective for enhancing reasoning capabilities, which are essential for both levels of CoT. Specifically, we first instruct the ULM to imagine and plan the image based on the prompt to obtain the semantic-level CoT. Then, we feed it into the ULM as the condition for the subsequent image generation for token-level CoT. We simultaneously generate multiple images from each prompt and then compute group-relative reward to optimize both levels of CoT within the same iteration. Unlike understanding tasks, where clearly defined rules for rewards exist, image generation lacks such standardized rules. Therefore, we propose to utilize an ensemble of diverse vision experts [90, 82, 49, 24] as reward models. This reward design serves two critical purposes: it evaluates generated images from multiple dimensions to ensure reliable quality assessment, while also functioning as a regularization method to prevent the ULM from hacking a single reward model.

Through the proposed reasoning strategies, we obtain T2I-R1 , the first reasoning-enhanced text-toimage model combining the semantic-level and token-level CoT. Empirical results show that our approach outperforms baseline models by 13% and 19% improvements on the T2I-CompBench and WISE benchmark, and even surpasses the previous state-of-the-art model FLUX.1. Qualitative analysis reveals that our method empowers the model to generate more human-aligned results by reasoning about the true intentions behind the prompt and demonstrates enhanced robustness when dealing with uncommon scenarios.

Our contributions are summarized as follows:

We identify a dual-level reasoning process in the autoregressive image generation task by introducing the semantic-level and token-level CoT, which decouple high-level image planning from low-level pixel generation for more reliable generation.

We develop BiCoT-GRPO, a new reinforcement learning framework that jointly optimizes both levels of CoT reasoning, seamlessly integrating the understanding capabilities of ULMs for image generation. For reward modeling, we investigate a robust reward system utilizing an ensemble of vision experts.

Our resulting model, T2I-R1, incorporates both levels of CoT using BiCoT-GRPO and demonstrates significant quantitative and qualitative improvements, surpassing FLUX.1 across multiple established benchmarks.

2 Method

2.1 Preliminary

Recently, the employment of reinforcement learning has been the dominant approach to elicit the reasoning capability of the large models. [73] introduces GRPO, enhancing PPO by eliminating the value function and estimating the advantage in a group-relative manner. For a specific prompt-answer pair ( p, a ) , a group of G individual responses { o i } G i =1 is sampled from the old policy π θ old . Each response is then input to a reward function to obtain the individual reward R i . Then, the advantage of the i -th response is calculated by normalizing the rewards {R i } G i =1 of the group:

GRPO adopts a clipped objective similar to PPO. Besides, a KL penalty term between the current policy π θ and the reference model π θ ref is directly added in the loss function:

where r i,j ( θ ) is the ratio between the probabilities of π θ and π θ old for outputting the current token:

In text reasoning tasks like mathematical problem solving, the model is instructed to follow the pre-defined template to output the reasoning process and final answer. The reward functions are rule-based rewards that only check the correctness of the final answer and the output format.

2.2 Semantic-level and Token-level CoT

In the autoregressive text generation tasks of LLMs and LMMs, CoT occurs in the textual reasoning format. However, in autoregressive image generation tasks, we identify two distinct types of CoT that could enhance the image generation at different abstraction levels:

Semantic-level CoT. Semantic-level CoT is defined as the textual reasoning that precedes image generation, serving as an overall semantic planning stage for the intended image. This process mirrors human artistic creation: when given a brief prompt, an artist first thinks about the scene construction, considering object attributes, spatial relationships, and interactions. In addition to the planning for common prompts, we also observe the semantic-level CoT benefits two other scenarios. If the prompt does not directly depict the object to generate, the semantic-level CoT can reason about the true intention from the user's prompt, providing more aligned images. As illustrated in Fig. 2, the semantic-level CoT reasons that the flower cultivated in the country where Amsterdam is located is tulip. Without this semantic-level CoT, Janus-Pro fails to provide valid results. Additionally, the semantic-level CoT demonstrates importance when handling unusual or potentially ambiguous scenes. In the bottom example of Fig. 2, when given the prompt 'A pig on the bottom of a train' , semanticlevel CoT introduces the action 'lying' for the pig, creating a more sensible scenario. In contrast, direct generation without this interpretive imagination creates significant confusion for Janus-Pro. Formally, each semantic-level CoT s i is composed of | s i | text tokens { s i, 1 , s i, 2 , ..., s i, | s i | } .

Token-level CoT. Unique to the image generation task, a token-level step-by-step thinking exists in the image generation process. The generation of image tokens much resembles a chain of thought: the image tokens are generated patch by patch, where the current patch is generated based on the previous ones. We define the sequential generation of image tokens as token-level CoT. This process parallels how an artist progressively fills a canvas, with the generated patches forming a visual reasoning chain. The reasoning content is the choice of the specific visual token for each patch, which corresponds to the patch coherence, object appearance, lighting conditions, and other visual details. Note that,

Unified LMM

Step 1

'a pink rabbit right of  a

green bowl with two

paintings at background.'

Step 2

Unified LMM

'Provide a brief, precise

visualization of  all

elements in the prompt…'

Decode

Token-level

CoT

Image Prompt

Reasoning Instruction

Human

Preference

Model

Object

Detector

VQA

Model

Output

Reward

Model

Semantic-level CoT

'A pink rabbit stands to the right of  a green bowl, their colors

creating a vivid contrast. ... Two background paintings add

artistic flair with varied styles. ... The rabbit keeps slight

distance from the bowl, balancing the composition. ...'

Rewards

Group Computation

Advantages

…

r

1

r

2

r

G

…

r

3

r

4

a

1

a

2

a

G

…

a

3

a

4

Group Relative Policy Optimization

G

for	GRPO

G

for	GRPO

G

for	GRPO

'a pink rabbit right ...'

Image Prompt

<img_start>

G

the reasoning occurs simultaneously with final output generation, rather than as separate steps. The model reasons and generates in parallel, integrating its thinking directly into the creation process. The generated chain of patches is later reshaped to a 2D grid G ∈ R h × w × c and input to an image decoder D to obtain the image. Unlike semantic-level CoT, which addresses global planning, token-level CoT focuses on local details and visual coherence across the image space. Formally, each token-level CoT t i consists of M image tokens { t i, 1 , t i, 2 , ..., t i,M } , where M represents the resolution of the generated image, i.e., M = h × w .

2.3 BiCoT-GRPO

GRPO has been proven to be highly effective for exploring the reasoning capability of the LLMs and LMMs. To accommodate both semantic-level and token-level CoT in image generation, we propose BiCoT-GRPO, where the model reasons twice in a single generation process. We instruct the model to first perform semantic-level CoT for global planning, and then dive into the local details by performing token-level CoT.

However, compared with the task of text generation, a great pipeline challenge is posed for incorporating two levels of CoT for image generation. Limited by the training paradigm, most current ULMs cannot generate interleaved images and text themselves. A manual signifier is often needed to instruct the model on which task to perform, either text generation or image generation. For Janus-Pro to generate an image, which is the ULM we use in this work, we need to manually concatenate an image start token ( <img_start> ) to explicitly instruct the model to start generating image tokens.

To tackle this problem, we propose a novel pipeline to facilitate ULM in generating images with two levels of CoT, as shown in Fig. 3. Specifically, our pipeline is composed of a two-step generation process. The first step is to generate the semantic-level CoT. We input the image prompt and instruct the model to imagine and reason about the details of the image to generate semantic-level CoT { s i } G i =1 . The second stage focuses on the token-level CoT generation. We input the image prompt, the generated semantic-level CoT in the first stage, and the image start token to the ULM for generating image tokens { t i } G i =1 . Then, the image tokens are input to the image decoder to obtain the

Human

Preference

Model

Object Detector

Prompt

&	Image

Reward:

1.00

o

Prompt	Alignment?

o

Aesthetic	Appeal?

Reward:

0.27

Reward:

0.6

High

'Yes'

Prob.

Low

'Yes'

Prob.

No

Yes

No

Yes

Output Reward

Model

Reward:

0.5

Image

GPT-4o

mini

Object

Object	w/	Attribute

o

Blue	Butterfly

o

Green	Candle

o

Butterfly

o

Candle

Object

&	Image

Object	w/

Attribute

&	Image

Prompt

&	Image

Prompt

'

A	blue	butterfly on the

left of a	green candle

.'

o

Object	Existence?

o

Spatial	Relation?

o

Blue	Butterfly?

o

Green	Candle?

o

Prompt	Alignment?

Low

'Yes'

Prob.

No

Yes

image I . Since there exist two types of CoT in our method, first the semantic-level CoT and then the token-level CoT. Each response o i is composed of two parts, namely o i = ( s i , t i ) . In this sense, the r i,j ( θ ) is converted to:

Then, we update the ULM by maximizing Equation 2.1. In practice, we incorporate the token-level policy gradient loss in [101], where the loss term is normalized over all the generated tokens to balance the reward on overly long semantic-level CoT.

2.4 Ensemble of Generation Rewards

Unlike DeepSeek-R1 with the rule-based reward, assessing the images based on pre-defined rules is infeasible. The assessment of the image includes various aspects, including the aesthetic appeal and objects' existence, attributes, and relationships. Considering the complexity, we introduce an ensemble of vision experts to judge the generated image from multiple aspects. Meanwhile, the use of multiple reward functions also serves as a regularization method to prevent the ULM from hacking into a specific reward model. As shown in Fig. 4, the ensemble contains the following experts:

Human Preference Model. Human preference models (HPMs), such as HPS [90] and ImageReward [94], are trained to simulate human aesthetic preferences. These models are developed using datasets of human rankings on synthetic images, where annotators evaluate and compare generated outputs. During inference, these models assess both the aesthetic quality and prompt alignment of a generated image, producing a composite human preference score R HPM. This expert provides a holistic reward signal from a general perspective.

Object Detector. Another option of the reward model is an object detector, e.g., GroundingDINO [49] and YOLO-world [12]. These open-vocabulary detection models accept an image along with object queries as input and output both the spatial positions and confidence scores for detected objects. This kind of vision expert serves as an ideal tool to evaluate the object's existence

Show a plant that is a

symbol of  good fortune in

Irish culture, and is known

for its three-lobed leaves

A specific type of  camera

used in the 19th century for

early photography

A bird grooming its feathers

Janus-Pro

Only

Semantic-level

Only

Token-level

T2I-R1

(Semantic-level

+Token-level)

Janus-Pro

Only

Semantic-level

Only

Token-level

Generate an image of  a

bird and a dog, with the

smaller animal on top and

the larger below

A typical dish from the

country where Naples is

located

The animal that emerges

from a cocoon, symbolizing

transformation

National Emblem of  the

country where New York is

located

A symbol of  imperial

China, a sprawling complex

of  palaces and temples in

Beijing

T2I-R1

(Semantic-level

+Token-level)

1mprqiol Chhioo

Efno Faaco

and relationship concerning space and numbers. For implementation, we extract all objects { obj i } K i =1 from the training image prompts, where K represents the total number of objects. We then query the object detector to identify these objects within the generated image. For each object, we assign a binary existence score (1 if detected, 0 otherwise) and average these scores across all objects in the prompt. If the prompt contains a spatial relationship, we further leverage the detected location to validate its correctness. We calculate the relative distance and intersection over union (IoU) between the objects for the spatial score R spatial. If the number of the object n obj i is specifically pointed out in the prompt, we compare the number with the detected number of the object ˆ n obj i . The reward from the object detector R Det is determined as:

where R spatial is 1 if the relative distance between the objects is larger than a threshold and the direction is right. If the direction is wrong, the reward is 0. Otherwise, we use the IoU as the spatial reward. We set α as 0.6 to encourage the correctness of the spatial relationship.

Visual Question Answering Model. The visual question answering (VQA) models are trained to answer questions based on the image input. The VQA models include earlier models prior to LLM, e.g., BLIP [42] and GIT [82], and LMMs like LLaVA [47]. We leverage these models to judge the existence and attributes of the objects. For example, if the image prompt is a red dog and a yellow cat , we first reformat each individual object with its attribute as a question to the VQA model, i.e., a red dog? and a yellow cat? . Then, we record the probability for the model to answer Yes as P i Yes and No as P i No . The reward for a prompt is calculated as: R VQA = 1 ∑ P i Yes i i .

Output Reward Model. Lastly, we also employ the output reward model (ORM) proposed in [24] as a reward model. The ORM is fine-tuned from an LMM (e.g., LLaVA-OneVision [39]) specifically for evaluating the alignment between the prompt and the image. The fine-tuning is to instruct the model to output Yes if the image perfectly aligns with the image and No otherwise. We calculate R ORM using the methodology similar to R VQA, except that we input the whole image prompt to the ORM instead of reformatting the prompt. The major difference between the ORM and HPMs is that the ORM model incorporates extensive world knowledge inside the LMM while HPMs mainly focus on the human preferences including prompt-image alignment and aesthetic appeal.

We can choose one or multiple reward functions illustrated above, and take the average as the final reward for a specific sample. The detailed experiments of reward model are shown in Table 3.

3 Experiment

In this section, we first provide the main results of T2I-R1 in T2I-CompBench [28], WISE [61] and GenAI-Bench [45] in Section 3.1. Then we present the results of different reward function combinations in Section 3.2 and the ablation study of the effectiveness of two levels of CoT in Section 3.3. Please refer to the Appendix B for TIIF-Bench [85] results, detailed experiment setup, and more visualizations.

3.1 Main Results

We compare T2I-R1 with leading text-to-image diffusion and autoregressive models on the T2ICompBench and WISE benchmarks (in Table 1, 2 and 4). We also provide the qualitative results in Fig. 5. Our method demonstrates substantial improvements over the baseline model, with average enhancements of 13% and 19% on T2I-CompBench and WISE, respectively. On T2I-CompBench, the most significant gains appear in attribute binding, with an average improvement of 19%. For the WISE benchmark, improvements are more evenly distributed across categories. When compared to the more powerful state-of-the-art diffusion models, T2I-R1 achieves superior or comparable results across both benchmarks. Notably, on T2I-CompBench, our method leads in five of six subtasks, with an exceptional performance in the spatial subtask (0.3378), surpassing previous SOTA results by over 5%. Similarly, for WISE, T2I-R1 excels in four of seven subtasks and achieves the highest overall score of 0.54, outperforming the robust FLUX.1-dev by 4%. Remarkably, our approach consistently achieves the leading results across all subtasks in both benchmarks when compared to other autoregressive models. Remarkably, the improvement on T2I-Compbench benefits from the planning ability brought by the semantic-level CoT, which designs the complex scenarios before generation. While the enhancement of WISE is due to the reasoning capability from the semanticlevel CoT, which deduces the true object or place depicted behind the prompt. For GenAI-Bench, T2I-R1 largely improves the baseline model, and in the meantime, achieves the highest overall score on both the basic and advanced prompts. Again, T2I-R1 surpasses FLUX.1 [37] in both types of prompts and showcases a remarkable margin in the advanced prompt, probably attributed to the high-level reasoning capability granted by semantic-level CoT.

3.2 Reward Analysis

In this section, we experiment with the choice of reward functions and their combinations. We hope to provide some insights into how to choose the reward functions and combine them. Our results are shown in Table 3. We first experiment with the individual reward model. HPM ( H ) demonstrates superior performance in attribute binding but shows limited effectiveness in object relationships, likely due to its weak relation comprehension capabilities. The object detector ( D ) yields the least improvement in attribute binding, which aligns with expectations since our detector-based reward functions do not explicitly evaluate attributes. The improvements observed stem solely from enhanced object existence ratios in the prompts. We observe that VQA model ( V ) and ORM ( O ) are

both effective reward models with distinct strengths: the VQA model excels at improving attribute binding, while ORM demonstrates superior performance in relationships. Then we experiment with multiple reward models. We start from the composition of HPM and object detector ( H + D ), and progressively incorporate other reward models. Our findings indicate that both the HPM-object detector combination ( H + D ) and the three-model integration of HPM, object detector, and VQA ( H + D + V ) deliver balanced and satisfactory results in both attribute and relationship tasks. To obtain the optimal choice of reward models, we conduct a human study to evaluate the visual quality, detailed in Appendix C.2. We adopt the combination of the highest visual quality, the ensemble of three reward models ( H + D + V ) for our final model.

Token-level CoT Only

Semantic-level + Token-level CoT

a black squirrel and a brown nut

3.3 Ablation Study

a fabric bag and a glass vase To validate the effectiveness of the semantic-level CoT, we compare T2I-R1 with a baseline method that generates images using only the token-level CoT optimized with the GRPO method. This is the default text-to-image generation setting in Janus, whose result is shown in the third row in Table 5. Comparing the third and fourth row in the table, we find that semantic-level CoT generally brings performance improvements across both benchmarks tested. We witness a particularly significant gain on the WISE benchmark. This enhanced performance can be attributed to the textual reasoning a key on the right of  a dog

capabilities inherent in semantic-level CoT. As illustrated in Fig. 5, our method could first clearly reason about the objects or phenomena described in the prompt through semantic-level CoT. This effectively decouples the reasoning and generation processes and thereby facilitates superior results. We also observe that training solely with token-level CoT substantially reduces the diversity of generated images, as demonstrated in Fig. 6, 7, 13, and 14. To quantify this effect, we evaluate image diversity by reusing the generated images from T2I-CompBench, where each prompt generates ten images. We compute the Vendi Score [18] across the ten images for each prompt. Results indicate that GRPO training without semantic-level CoT decreases the diversity score, whereas incorporating semantic-level CoT significantly improves diversity through varied textual planning.

We also consider another situation to validate the effectiveness of token-level CoT: the semantic-level CoT is incorporated in the image generation process, as T2I-R1, but GRPO only optimizes the semantic-level CoT without the token-level CoT. This can be viewed as only enhancing the model's high-level planning capabilities. The second row of Table 5 presents the result. The results show that optimizing semantic-level CoT exclusively yields smaller improvements compared to the joint optimization approach. Additionally, we find that optimizing both CoT types produces images with much better aesthetic quality compared with optimizing semantic-level CoT only, as shown in Fig. 5. This indicates the necessity to jointly optimize both levels of CoT.

4 Conclusion

In this paper, we introduce T2I-R1, the first reasoning-enhanced text-to-image model powered by a bi-level CoT reasoning process. We identify the semantic-level CoT for high-level planning and the token-level CoT for patch-by-patch generation. We further integrate them through our proposed BiCoT-GRPO, an RL framework incorporating two levels of CoT within the same training step. By leveraging a ULM capable of both visual understanding and generation, our approach eliminates the need for separate specialized models while achieving significant performance improvements, +13% on T2I-CompBench and +19% on the WISE benchmark. Our qualitative analysis demonstrates that T2I-R1 better understands complex prompts, reasons about user intentions, and handles uncommon scenarios with greater robustness, establishing a new paradigm for text-to-image generation tasks.

Acknowledgments

This study was supported in part by National Key R&D Program of China Project 2022ZD0161100, in part by the Centre for Perceptual and Interactive Intelligence, a CUHK-led InnoCentre under the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government, in part by NSFC-RGC Project N_CUHK498/24, and in part by Guangdong Basic and Applied Basic Research Foundation (No. 2023B1515130008, XW).

Additional support was provided by the Research Grants Council of the Hong Kong Special Administrative Region, China, under Project 14201321 and Project 14200824.

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Zong, Z., Jiang, D., Ma, B., Song, G., Shao, H., Shen, D., Liu, Y., Li, H.: Easyref: Omnigeneralized group image reference for diffusion models via multimodal llm. arXiv preprint arXiv:2412.09618 (2024)

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A Related Work

Unified Generation and Understanding LMM. Recently, the effort to unify image generation and understanding in a single LMM has attracted much attention. Building upon large language models (LLMs), it is natural for the LMMs to understand the image and output the text [63, 39, 109, 19, 104, 33]. However, the method of how to generate an image from a LMM is still under exploration. The image generation method diverges into different branches. One line of the method relies on an exterior image generation model to complete generation [15, 78, 77, 43, 80, 17, 108, 38]. The generator often utilizes text-to-image diffusion models [70, 66] due to its powerful generation capability. To deliver the generation information, the LMM passes either the implicit conditional feature or the explicit image prompt to the generator. For example, EMU [78] first trains the LMM to output CLIP [68] image features identical to that input to the LMM. Then, a pretrained UNet [71] of Stable Diffusion [70] receives the output feature as the condition to generate an image. Another line of the method seeks to train the LMM to generate discrete tokens produced by VQGAN [16] to eliminate the need for an additional generator. [83, 41] directly adopts the VQGAN encoder as the image tokenizer for LMM. However, the VQGAN encoder is only pretrained on the image reconstruction task and thereby generates visual tokens less helpful for image understanding. To improve the understanding capability, [86, 9, 57, 48] proposes to tackle the understanding and generation tasks with different vision encoders separately. The CLIP encoder deals with image input for understanding, while the VQGAN encoder is responsible for generation. Moreover, some works [91, 67, 75] attempt to empower the vision encoder with both the understanding and the generation capability. VILA-U [91] trains a vision encoder with both the contrastive loss [68] for text-image understanding and reconstruction loss [16] for image detail preserving. Thanks to the joint pretraining, the vision encoder could generate text-aligned discrete visual tokens. The LMM is then trained to receive the discrete tokens for image understanding and predict them for image generation.

Reinforcement Learning for Large Reasoning Models. The emergence of OpenAI o1 [65] has gained tremendous attention in developing the reasoning capability of large language models. Later, DeepSeek-R1 [20] proposes a rule-based reward and GRPO training method. The introduced method instructs the model to perform an extensive reasoning process before generating the final answer. The reward only focuses on the correctness of the final answer and the following of the pre-defined format. Recently, a number of works have applied this method to multi-modal large language models [7, 59, 97, 103, 14, 29, 11] with task-specific rewards like correctness and IoU [50]. This training paradigm largely helps various reasoning-intensive tasks [69, 32, 23] like mathematical problem-solving [27, 58, 51, 105, 106], code generation [8, 2, 30], and complex scene understanding [98].

B More Experiment Details

B.1 Experiment Setup

Training Settings. Our training dataset comprises text prompts sourced from the training set of T2I-CompBench [28] and [24], totaling 6,786 prompts with no images. Prior to training, we use GPT4o mini to extract the objects and their attributes from the prompts to facilitate computing the rewards. We use Janus-Pro-7B as the base model. We use a learning rate of 1e-6 and a beta of 0.01. For the reward model, we choose HPS [90] as the human preference model, GroundingDINO [49] as the object detector, and GIT [82] as the VQA model. For the ORM, we finetune LLaVA-OneVision-7B in the same manner as [24].

Benchmark. We test on T2I-CompBench [28], WISE [61], GenAI-Bench [45], and TIIFBench [85] to validate the effectiveness of our method. T2I-CompBench comprises 6,000 compositional text prompts evaluating three categories (attribute binding, object relationships, and complex compositions) and six sub-categories (color binding, shape binding, texture binding, spatial relationships, non-spatial relationships, and complex compositions). WISE consists of 1,000 text prompts spanning three categories (cultural common sense, spatial-temporal reasoning, and natural science) for evaluating world knowledge of the text-to-image models. To correctly generate an image, the model needs to reason about what the exact object or scenario is depicted in the prompt. We slightly modify the reasoning instruction on the WISE benchmark for more aligned results. GenAI-

Semantic-level + Token-level CoT

a fabric bag and a glass vase

a key on the right of  a dog

Model

Overall

Basic Following

Advanced Following

Designer

Avg

Attribute

Relation

Reasoning

Avg

Attribute

+Relation

Attribute

+Reasoning

Relation

+Reasoning

Style

Text

Real

World

short

long

short

long

short

long

short

long

short

long

short

long

short

long

short

long

short

long

short

long

short

long

short

long

Llamagen [76]

41.67

38.22

53.00

50.00

48.33

42.33

59.57

60.32

51.07

47.32

35.89

32.61

38.82

31.57

40.84

47.22

49.59

46.22

46.67

33.33

0.00

0.00

39.73

35.62

LightGen [89]

53.22

43.41

66.58

47.91

55.83

47.33

74.82

45.82

69.07

50.57

46.74

41.53

62.44

40.82

61.71

50.47

50.34

45.34

53.33

53.33

0.00

6.83

50.92

50.55

Show-o [93]

59.72

58.86

73.08

75.83

74.83

79.83

78.82

78.32

65.57

69.32

53.67

50.38

60.95

56.82

68.59

68.96

66.46

56.22

63.33

66.67

3.83

2.83

55.02

50.92

Infinity [25]

62.07

62.32

73.08

75.41

74.33

76.83

72.82

77.57

72.07

71.82

56.64

54.98

60.44

55.57

74.22

64.71

60.22

59.71

80.00

73.33

10.83

23.83

54.28

56.89

Janus-Pro [9]

66.50

65.02

79.33

78.25

79.33

82.33

78.32

73.32

80.32

79.07

59.71

58.82

66.07

56.20

70.46

70.84

67.22

59.97

60.00

70.00

28.83

33.83

65.84

60.25

T2I-R1 (Ours)

68.59

67.19

82.90

81.63

86.50

83.00

83.47

79.43

78.73

82.46

69.05

68.00

71.64

69.47

72.43

69.95

69.40

70.40

60.00

63.33

27.60

26.24

67.54

60.45

Bench is a benchmark containing 1,600 complex, real-world text prompts collected from professional designers, which covers a broad spectrum of compositional text-to-visual generation elements, from basic aspects like scenes, attributes, and relationships to more professional ones, including counting, comparison, differentiation, and logical reasoning. TIIF-Bench is a comprehensive benchmark for fine-grained text-to-image model evaluation, featuring 36 novel prompt combinations across six compositional dimensions and 100 real-world designer-level prompts with rich aesthetic judgment. We follow the official evaluation setting of all the benchmarks.

C More Experiment Results

C.1 More Results

We provide the experiment results on TIIF-Bench in Table 6 and more qualitative examples in Fig. 8.

Finally, we discuss the zero-shot potential of the baseline model to perform both semantic-level and token-level reasoning. Specifically, we apply the same image generation process of T2I-R1 directly to the baseline model, where the baseline model is first instructed to output the semantic-level CoT and then the token-level CoT. We term this method of generation as 'Janus-Pro w/ zero-shot semantic-level CoT' in Figure 9-12. As shown in the figure, zero-shot semantic-level CoT brings very marginal improvement, while T2I-R1 demonstrates a satisfying result. The reasons are twofold: (1) Zero-shot semantic-level CoT misses critical objects in the original prompt. As shown in Figure 12, the zero-shot semantic-level CoT misses the bird in the original prompt. (2) Zero-shot semantic-level CoT does not fit the model's generation ability or provide useful information for generation. Although

Janus-Pro

Only

Semantic-level

Only

Token-level

A chameleon perfectly camouflaged against a green leaf

A chameleon perfectly camouflaged against a brown leaf

Janus-Pro

Only

Semantic-level

Only

Token-level

A specific type of  aircraft used in World War II

Copper wire exposed to air for a long time

A famous flower that symbolizes wealth in China

An animal with a long nose

A traditional mode of  transportation in Egypt

A flower with deep cultural and religious significance in India,

representing purity and beauty

A massive stone statue of  a mythical creature that is a prominent

historical landmark in Egypt

A form of  personal communication before the advent of  telephones

T2I-R1

(Semantic-level

+Token-level)

T2I-R1

(Semantic-level

+Token-level)

the semantic-level CoT in Figure 9-11 includes all the objects and relationships, the baseline model still fails to generate a satisfying result. This highlights the necessity of our proposed BiCoT-GRPO training method to build the synergy between the two levels of CoT and make them work together.

C.2 More Details about Reward Analysis

We conduct a human study to evaluate the visual quality of the generated images. Specifically, we select four options of reward models ( V , O , H + D , and H + D + V ) to generate an image from the same prompt. Then we ask humans to rank the four images and score them according to the rank (rank 1 for 3 points, rank 2 for 2 points, and so on). We ask the annotators to judge only according to the visual appeal. We provide examples of an unsatisfactory generation, including different aspects: 1) Corrupted floating words, 2) Distortion like melting effect of faces, hands, or other objects, 3) Extra or missing hands, legs, tails, etc., 4) Object merging, frequently observed for compositional prompts, 5) Low-level aspects including over exposure or over saturation, 6) Blurred areas, especially in fine details like keyboard. The instruction to the annotator highlights that the highly ranked images should show no or fewer problems compared with others. Eight graduate students are employed to conduct the study to eliminate individual bias. We randomly choose 30 prompts from each of the subtasks from the T2I-CompBench. The result is shown in the visual quality column in Table 3. We observe that ensemble rewards achieve better visual quality, with H + D + V obtaining slightly superior results. This improvement could be attributed to the implicit regularization provided by multiple rewards, preventing overfitting to a single reward model. Conversely, individual reward models fail to provide satisfactory quality despite high benchmark scores.

C.3 Comparison with Prompt Rewriting Methods

The prompt rewriting [96, 102, 26, 88] is a method to leverage an external LLM to generate an enriched version of the prompt and use this version for the text-to-image model to produce the images. Our semantic-level CoT design resembles the high-level idea of this method, but there exist several key differences. First, the semantic-level CoT is generated from the image generation model itself, while the prompt enrichment leverages an extra LLM. Second, our design facilitates a joint-optimization of the prompt design and the image generation. On the contrary, the prompt enrichment is generation-model-agnostic, which means that although the prompt is enriched with more details, it is not necessary that the generation model can generate a better image based on this enriched prompt. We validate this claim in Figure 9-11. Although the zero-shot semantic-level CoT (enriched prompt) all correctly includes all the key objects mentioned in the prompt and adds more details, the model still cannot generate satisfying result. Simply enriching the prompt is not sufficient to bring notable improvements.

C.4 Comparison with Training with RL and Supervised Finetuning

In our experiments, we directly employ reinforcement learning. An alternative is to first conduct supervised finetuning (SFT) and then continue with RL training. Our findings show that a cold start stage with SFT seems to be detrimental for the final performance. Specifically, we use the open-source high-quality text-to-image dataset BLIP3o-60K [5] as our SFT dataset. We employ Qwen2.5-VL-72B-Instruct [3] to generate the semantic-level CoT with the image and the original short prompt input. We finetune the model for one epoch and then conduct RL finetuning following the same training setting of T2I-R1. The results are shown in Table 7 below:

The key findings are two-fold. First, high-quality SFT is beneficial. The curated BLIP3-60k dataset yields significant performance gains. However, SFT performance substantially lags behind RL training. We hypothesize this occurs because SFT constrains the model to replicate the training distribution rather than leveraging its inherent capabilities. Specifically, when presented with a valid semantic-level CoT, SFT forces the model to generate the exact corresponding training image, even when the model could produce alternative valid outputs. This constraint introduces unnecessary training complexity. Second, RL after high-quality SFT is still inferior to direct RL training. While high-quality SFT pre-training improves subsequent RL performance, the combined approach remains mostly inferior to or merely comparable with direct RL training.

C.5 Choice of Reward Weights

In Table 1, the weight of all the rewards are set to 1 . We find that the weight of the reward model has little influence on the final training result. Here we provide the detailed study to illustrate the our method is stable to the weight of the reward model. We follow the training setting of T2I-R1, where three reward models, HPS, GroundingDINO, and GIT are employed. In our experiments, we multiply the weight of each reward model by 5 respectively, while maintaining the weight of the other two reward models. Apart from these three experiments, we also conduct an experiment where we compute the relative reward of each reward model inside the group, and then sum up the normalized reward as the final reward for the sample. This eliminates the mean and variance difference among the reward models (termed as Normalized Reward). The results are shown in Table 8 below.

C.6 Hyperparameters

All of our experiments are conducted on 8 H800. Our training procedure lasts about 16 hours. We provide the detailed training hyperparameters in Table 9.

D Limitations and Future Work

While this work explores the text-to-image generation task, it requires more exploration on how to apply this paradigm to other modalities like video generation [4] or 3D [21] tasks. Specifically, video generation tasks are more complex regarding the reward design and the base model. For the reward design, how to apply dense rewards on each generated frame is still an open question. Besides, there exists no understanding and generation unified model for videos, so BiCoT-GRPO cannot be used directly. Meanwhile, the current inference time of video generation is too long for the current GRPO paradigm. How to balance the training time and effect needs further study.

Prompt

Janus-Pro

Zero-shot Semantic-level CoT

T2I-R1 Semantic-level CoT

a red train, a blue bowl, a green flower, and a yellow cake

Janus-Pro w/ zero-shot semantic-level CoT

Underneath a blue sky, a red train is moving along the railway track. Next to it, a green flower stands tall, while a yellow cake sits on a nearby table.

In the image, the red train occupies the left side of the image, with a sleek and polished metallic finish. Beside it, a blue bowl is sitting gently on a wooden table, its surface reflecting light, and there lies a delicate green flower on the table, its petals spreading like a butterfly's wings. In the background, an appetizing yellow cake with an intricate icing decoration stands next to a table on the right side of the scene. The train appears slightly ahead of the bowl, while the cake sits further behind the table and flower, creating an interesting spatial arrangement of objects and colors against a bright backdrop.

Figure 9: Visualization Results of Semantic-level CoT. We provide the image generation results of the same prompt from three settings: base model, base model with zero-shot semantic-level CoT, and T2I-R1. For the setting of base model with zero-shot semantic-level CoT, we use the same generation pipeline of T2I-R1 directly on the base model. We employ the same prompt of T2I-R1 to instruct the base model to generate a zero-shot semantic-level CoT, which we visualize in the figure and provide a comparison of the semantic-level CoT generated by T2I-R1.

Janus-Pro Prompt

T2I-R1 Semantic-level CoT

three paintings and one knife

Janus-Pro w/ zero-shot semantic-level CoT

Three paintings hung on the wall, and in the middle stood a sharplooking knife. The three paintings were of varying nature: one depicted sunflowers in a vibrant yellow, the second portrayed a serene countryside with soft blues and greens, while the third had an abstract, multicolored design.

The image displays three paintings hung on a wall, each characterized by vibrant colors such as pink, blue, and yellow, reflecting various artistic styles. In front of the paintings, there is a single, slender knife with a metallic polish, positioned horizontally and lying on a polished wooden surface. The knife's reflective glare contrasts with the richness of the paintings, creating an intricate visual interplay. The paintings provide a backdrop to the knife, and their colors accentuate the sharpness of the blade.

Figure 10: Visualization Results of Semantic-level CoT. We provide the image generation results of the same prompt from three settings: base model, base model with zero-shot semantic-level CoT, and T2I-R1. For the setting of base model with zero-shot semantic-level CoT, we use the same generation pipeline of T2I-R1 directly on the base model. We employ the same prompt of T2I-R1 to instruct the base model to generate a zero-shot semantic-level CoT, which we visualize in the figure and provide a comparison of the semantic-level CoT generated by T2I-R1.

Prompt

Janus-Pro

Zero-shot Semantic-level CoT

T2I-R1 Semantic-level CoT

a dog behind the door

Janus-Pro w/ zero-shot semantic-level CoT

A dog is standing behind a door, with brown fur, dark eyes, a black collar, and a white patch on its face. The door is wooden, with a metal handle and hinges. The dog's ears are perked up, and it looks curious or alert through the door.

The image shows a brown and white dog peeking its head through a small gap in the door , with a gentle and curious expression etched onto its face. The door is slightly ajar, and the dog's body is barely visible beyond the edge of the frame , hinting at a playful waiting game. The room behind the door is dimly lit, creating an eerie yet intimate atmosphere as if the dog is on a mission or has been guarding the entrance for some time. The door itself is wooden with a simple metal handle. The overall tone of the visual emphasizes curiosity and anticipation.

Figure 11: Visualization Results of Semantic-level CoT. We provide the image generation results of the same prompt from three settings: base model, base model with zero-shot semantic-level CoT, and T2I-R1. For the setting of base model with zero-shot semantic-level CoT, we use the same generation pipeline of T2I-R1 directly on the base model. We employ the same prompt of T2I-R1 to instruct the base model to generate a zero-shot semantic-level CoT, which we visualize in the figure and provide a comparison of the semantic-level CoT generated by T2I-R1.

A fluffy white dog with bright amber eyes sits by the edge of a serene lake, observing a vibrant black and white bird soaring above. Nearby, a classic building in shades of brown and cream stands tall with numerous windows, giving it an old-world charm. Together, they create a picturesque scene that evokes peace and charm.

Janus-Pro

a dog, a bird, a lake, and a building

Janus-Pro w/ zero-shot semantic-level CoT

T2I-R1

Prompt

A dog wags its tail, plays fetch at the lake, and sits in front of the building.

T2I-R1 Semantic-level CoT

Zero-shot

Semantic-level CoT

Figure 12: Visualization Results of Semantic-level CoT. We provide the image generation results of the same prompt from three settings: base model, base model with zero-shot semantic-level CoT, and T2I-R1. For the setting of base model with zero-shot semantic-level CoT, we use the same generation pipeline of T2I-R1 directly on the base model. We employ the same prompt of T2I-R1 to instruct the base model to generate a zero-shot semantic-level CoT, which we visualize in the figure and provide a comparison of the semantic-level CoT generated by T2I-R1.

Janus-Pro

T2I-R1

origami cranes unfolding into real birds during cherry blossom season

Janus-Pro

T2I-R1

paint drops falling from brushes creating flowers on a canvas below

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces T2I-R1, a novel text-to-image generation model that pioneers a bi-level Chain-of-Thought (CoT) reasoning process. By effectively decoupling high-level semantic planning from low-level, patch-by-patch synthesis and optimizing both via a robust reinforcement learning framework (BiCoT-GRPO), the authors present a highly effective and well-validated method. The work's core concept is innovative, its experimental results are compelling—achieving state-of-the-art performance on multiple benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n1. **Novel Concept**: Introduces an innovative \"bi-level Chain-of-Thought\" (CoT) framework.\n2. **Thorough Experiments**: Features comprehensive ablation studies validating each component.\n3. **Smart Reward System**: Uses a robust ensemble of \"vision experts\" to guide RL training effectively.\n\n# Weaknesses\nThe term \"Token-Level CoT\" for the standard autoregressive, patch-by-patch generation process can be seen as a loose analogy. While the authors define it based on its sequential and conditional nature the \"thought\" aspect is less explicit compared to the semantic-level CoT. Since there is no explicit thinking process, just a patch-by-patch image generation process.\n\nQUESTIONS:\n# Questions\n1. About Semantic-Level CoT\n\nFor simple, unambiguous prompts (e.g., 'a red apple'), could the authors discuss how the model avoids 'over-planning'—where the generated textual details might unnecessarily constrain the model's stochastic creativity in aspects like lighting, texture, and composition?\n\n2. About the analogy on 'Token-Level CoT'\n \nThe paper terms the patch-by-patch generation process 'Token-Level CoT,' drawing an analogy to textual CoT. Could the authors further elaborate on what distinguishes this process from standard autoregressive generation, beyond the inherent sequential dependency? For instance, textual CoT often involves intermediate steps that are human-interpretable and logically verifiable. How does this core aspect of 'thought' translate to the token-level visual generation? A deeper conceptual clarification would help solidify the paper's contribution in this regard.\n\n3. About Visual Quality\n\nTo further strengthen the analysis on the impact of BiCoT-GRPO on Visual Quality, could the authors provide more details on the human study protocol? The paper's primary contribution is enhancing compositional reasoning. Could the authors offer a more explicit discussion on how the BiCoT-GRPO framework specifically impacts aesthetics as a distinct objective? For example, is there a risk that the RL process might overly prioritize compositional correctness at the expense of aesthetic quality in some cases, and how does the ensemble reward system mitigate this potential conflict?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces T2I-R1, the first reasoning-enhanced text-to-image generation model that applies chain-of-thought (CoT) reasoning to visual generation. This work bridges the gap between language model reasoning capabilities and visual generation, showing that explicit reasoning processes can significantly enhance image generation quality and prompt alignment.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. First to identify and formalize two distinct types of reasoning (semantic-level and token-level) that mirror human artistic creation process. The dual-level approach addresses both global planning and local detail generation.\n2. The proposed approach achieves significant quantitative improvements: +13% on T2I-CompBench, +19% on WISE benchmark.\n\nWeaknesses:\n1. The two-step generation process requires generating semantic COT, then image tokens, increasing inference time. Doesn't compare against using a dedicated LLM for planning + separate image generator\n2. Ensemble of multiple expert models is applied to prevent reward hacking. However, it requires careful tuning of multiple reward models and their combinations. The importance of each reward model is not clear in Table 3.\n3. Multi-stage training with RL makes reproduction challenging.\n4. The work lacks insights into RL training and optimization. Overall, it appears to be a straightforward application of the GRPO algorithm to text-to-image tasks, lacking fundamental innovation.\n\nQUESTIONS:\n1. The computational overhead and complexity of the two-step generation process and ensemble reward system, which may limit practical deployment and accessibility. \n2. The work lacks insights into RL training and optimization. Overall, it appears to be a straightforward application of the GRPO algorithm to text-to-image tasks, lacking fundamental innovation.\n\nLIMITATIONS:\nNo", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes T2I-R1, a reasoning-enhanced text-to-image generation framework that integrates reinforcement learning (RL) with bi-level Chain-of-Thought (CoT) reasoning. It introduces two levels of CoT: semantic-level CoT for high-level prompt planning, and token-level CoT for fine-grained pixel-wise generation. To unify these two reasoning processes, the authors design BiCoT-GRPO, a training mechanism with an ensemble of generation rewards to jointly optimize both levels. Applied to the Janus-Pro baseline, T2I-R1 achieves significant performance improvements, outperforming prior state-of-the-art methods by 13% on T2I-CompBench and 19% on WISE.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths \n\n1. The paper introduces a novel bi-level reasoning paradigm for image generation, distinguishing between semantic-level and token-level CoT processes.\n\n2. It proposes a reward-driven optimization strategy for RL-based image generation, which is well-aligned with the model's hierarchical reasoning structure.\n\n3. The approach is empirically validated on multiple benchmarks, demonstrating performance improvements over strong baselines.\n\n\nWeaknesses\n\n1. The semantic-level CoT aims to enrich the initial prompt by imagining more detailed aspects of the target image. However, this idea closely relates to prior work on prompt enrichment in text-to-image generation, which is not discussed.\n\n- Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs\n- Prompt Refinement with Image Pivot for Text-to-Image Generation\n- DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation\n- Paragraph-to-Image Generation with Information-Enriched Diffusion Model\n\n2. The paper assumes a patch-by-patch generation process similar to auto-regressive models and maps this to token-level CoT. However, this assumption may not hold for diffusion-based generation, which follows a denoising trajectory rather than token-wise generation. Whether token-level CoT is meaningful or effective under diffusion paradigms is unclear. Alternative strategies, such as coarse-to-fine generation (e.g., as in VAR or Next-Scale Prediction), might be more appropriate and should be discussed.\n\n- Scalable Image Generation via Next-Scale Prediction\n\n3. The role of reinforcement learning in this framework needs to be further justified. It’s unclear whether RL is essential for the proposed model or whether similar gains could be achieved through supervised or hybrid optimization.\n\n4. Experimental results suggest that most performance gain comes from the semantic-level CoT. However, it remains unclear how much of this improvement comes purely from prompt enrichment, without involving RL or token-level reasoning. A fairer comparison would include a model with enriched prompts but no further optimization, especially in the context of diffusion-based generators.\n\nQUESTIONS:\nsee Weakness.\n\nLIMITATIONS:\nsee Weakness.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper introduces T2I-R1, which aims at performing text-to-image generation with bi-level CoT reasoning process. Specifically, the semantic-level CoT captures high-level semantics while the token-level CoT is for low-level information modelling. The proposed method BiCoT-GRPO boosts the performance of Janus-Pro by 13% on T2I-CompBench. The ablation verifies the effectiveness of different levels of the CoT reasoning process.

STRENGTHS AND WEAKNESSES:
**Strengths**
1) The motivation is clear, and the exploration of RL into text-to-image generation is worthwhile. The proposed methods sound technically correct. The CoT reasoning-based generation helps improve fidelity significantly. 
2) The BiCoT-GRPO unifies the semantic-level CoT and low-level CoT in a sequential manner, which leverages the semantic-enriched prompt for generation, exhibiting high quality.
3) The ensemble of generation reward models, including the object detector, VQA, HPS, and ORM helps comprehensively improve the generation quality in different aspects.

**Weakness**
1) In Table 3, I think the evaluation of the object detector on T2I-CompBench can not fully showcase the benefit of the reward design. I suppose the metrics counting in GenEval can better illustrate its effectiveness.
2) In Figure 5, if only included token-level CoT is included, the generated image is relatively similar to the original Janus-Pro output. It seems like the semantic-level CoT benefits more.
3) The function of the human Preference Model and Output Reward Model seems to overlap.

QUESTIONS:
The topic of the paper is worth investigating, and the paper well illustrates the benefit of RL in text-to-image generation. While some weaknesses above, I still think this paper is acceptable.

LIMITATIONS:
yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02