RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning
Authors: Mingrui Wu
Lu Wang
Pu Zhao
Fangkai Yang
Jianjin Zhang
Jianfeng Liu
Yuefeng Zhan
Weihao Han
Hao Sun
Jiayi Ji
Xiaoshuai Sun
Qingwei Lin
Weiwei Deng
Dongmei Zhang
Feng Sun
Rongrong Ji
Venue: ICLR 2026 Poster (2026)
Keywords: Reasoning; T2I; RL
Primary area: generative models

Abstract: Despite recent progress in text-to-image (T2I) generation, existing models often struggle to faithfully capture user intentions from short and under-specified prompts. While prior work has attempted to enhance prompts using large language models (LLMs), these methods frequently generate stylistic or unrealistic content due to insufficient grounding in visual semantics and real-world composition. Inspired by recent advances in reasoning for language model, we propose RePrompt, a novel reprompting framework that introduces explicit reasoning into the prompt enhancement process via reinforcement learning. Instead of relying on handcrafted rules or stylistic rewrites, our method trains a language model to generate structured, self-reflective prompts by optimizing for image-level outcomes. The tailored reward models assesse the generated images in terms of human preference, semantic alignment, and visual composition, providing indirect supervision to refine prompt generation. Our approach enables end-to-end training without human-annotated data. Experiments on GenEval and T2I-Compbench show that RePrompt significantly boosts spatial layout fidelity and compositional generalization across diverse T2I backbones, establishing new state-of-the-art results. Code: https://github.com/microsoft/DKI_LLM/tree/main/RePrompt.

Published as a conference paper at ICLR 2026
REPROMPT: REASONING-AUGMENTED REPROMPT- ING FOR TEXT-TO-IMAGE GENERATION VIA REIN-
FORCEMENT LEARNING
Mingrui W Lu Wang" Pu Zhao² Fangkai Yang"!, Jianjin Zhang", Jianfeng Liu", Yuefeng Zhan?, Weihao Han?, Hao Sun", Jiayi J Xiaoshuai Sun', Qingwei Lin", Weiwei Deng", Dongmei Zhang", Feng Sun Qi Zhang", Rongrong Ji
1 Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China.
2 Microsoft. 3 Zhongguancun Academy, Beijing, China. 100094.
ABSTRACT
Despite recent progress in text-to-image (T2I) generation, existing models of- ten struggle to faithfully capture user intentions from short and under-specified prompts. While prior work has attempted to enhance prompts using large lan- guage models (LLMs), these methods frequently generate stylistic or unrealistic content due to insufficient grounding in visual semantics and real-world compo- sition. Inspired by recent advances reasoning for language model, we propose RePrompt, novel reprompting framework that introduces explicit reasoning into the prompt enhancement process via reinforcement learning. Instead of relying handerafted rules stylistic rewrites, method trains language model to generate structured, self-reflective prompts by optimizing for image-level out- comes. The tailored reward models assesse the generated images in terms of hu- man preference, semantic alignment, and visual composition, providing indirect supervision to refine prompt generation. Our approach enables end-to-end train- ing without human- annotated data. Experiments on GenEval and T21-Compbench show that RePrompt significantly boosts spatial layout fidelity and compositional generalization across diverse T21 backbones, establishing new state-of-the-art results. Code is available at: https:// cwm/moioooooooooo wttp///mcrooff///tt LLM/trea ma in Pewp/rpPPptttt
INTRODUCTION
Text-to-image (T2I) generation has made rapid progress with the rise of large-scale generative mod- els Labs (024)) Esser al. (2024); Podell al. (2023); Chen al. (2024b), yet a persistent chal- lenge remain:: users typically provide concise and under-specified prompts, which often result in images that fail reflect the intended semantics or visually coherent compositions. Generated out- puts may misrepresent object counts, overlook spatial relations, or violate real-world plausibility. This misalignment arises from the gap between natural language descriptions and the structured visual logic required for faithful image generation Yang et al. (2024b).
Previous work on prompt enhancement in T2I can divided into two main approaches. The first approach focuses on iterative refinement: image is generated from initial prompt, and subse- quent feedback, derived either from human preference models or from automated scoring systems, is used to improve the prompt intermediate representations over multiple rounds Yang et al. (2024d); Wu et al. (2024); Wang et al. (2024); Guo et al. (20255). Although this approach can progressively improve image quality, it suffers from high latency and computational overhead due repeated im- age generation, and rarely incorporates explicit scene semantics or compositional reasoning. The second approach enriches prompts in a single pass by leveraging large language models (LLMs) to inject additional detail and context [Betker et al. (2023); Hao et al. (2023). While these methods pro- duce linguistically fluent and expressive descriptions, they frequently generate prompts that produce
Work done microsoft. +Project Leader. Conderr,Coonn |Corresponding author.

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User Prompt
photo of couch below vase
Prompt A photo-realistic image of cozy couch placed below an elegunt vase, with soft lighting and a warm, inviting atmosshere.
(a) Poor physical grounding
DALL-E-S
Reasoning
Let's break it down... The vase could be placed on shelf floating counter
PUO
couch
(b) Rich language but unrealistic
Enhanced Prompt
RePrompt
A living room where a large couch stands against wall. Above the couch, there's a floating wooden shelf on which a beautiful ceramic vase is placed ...
(c) Realistic semantically alignment
Given the user prompt "a photo of couch below a vase", existing models like DELL-E3 generate rich language descriptions but often produce unrealistic physically implausible compo- sitions. In contrast, our RePrompt performs explicit chain-of-though reasoning to resolve spatial relations, resulting in enhanced prompts that guide text-to-image models towards realistic and mantically aligned generations.
Figure 1:
images with semantically inconsistent or visually implausible content, such as conflicting object placements unrealistic interactions, because the underlying LLMs lack grounding in physical re- ality and do not incorporate feedback from downstream visual tasks. As a result, they frequently hallucinate content or miss critical spatial and attribute-level relationships (see Figure T.
In contrast, we propose RePrompt, a seasoning-augmeene prompt refinement framework trained via reinforcement learning. Rather than relying stylistic rewriting black-box completions, RePrompt trains language model to generate structured and semantically grounded prompts through self-reflection and step-by-step decomposition. Motivated by recent advances in reasoning- augmented language models Guo et al. (2025a); Trung et al. (2024); [Tam et al. (2025); ; Jaech al. (2024). RePrompt enables the model to internally simulate the visual implications of prompt-much mucch like how humans mentally visualize scene before drawing. This structured, logic- driven process anticipates potential errors (e.g., conflicting object positions, missing entities, or spatial incoherence) during prompt construction, thereby reducing the need for multiple rounds of image generation.
A core component of RePrompt is T2I RePrompt Reward Model tailored for text-to-image generation. Instead of relying on pre-labeled reasoning traces or handcrafted prompt templates, RePrompt learns from downstream visual feedback by optimizing prompt generation through inforcement learning. To capture the multifaceted nature image quality, we design ensemble reward that evaluates generated images along three dimentions: human preference, visual realism, and semantic alignment with the input. By learning from diverse and grounded feedback signals, the model develops more robust reasoning strategy that transfers across prompt types, scene structures, and T2I backbones, enabling stronger performance on unseen inputs without overfitting to specific linguistic patterns.
Experiments on GenEval Ghosh et al. (2023) and 121-Compbench Huang et al. (2033) demonstrate that RePrompt significantly improves compositional accuracy, semantic fidelity, and spatial coher- ence. Notably. on the GenEval benchmark, RePrompt surpasses Qwen2.5 3B-enhanced basclines by +77.1% (FLUX Labs) 20244), +78.8% (SD3 Esser al. (0044) and + +122.2% (Pixart- Chen et al. (024bb) in the position category, highlighting its superior capability in grounding spatial relations. Furthermore, RePrompt achieves the best overall accuracy (0.76) among all evaluated methods while maintaining ordgn-ef-magnitude lower latency than optimization-heat baselines like Idea2Img (30s vs. 140s per image), offering a scalable and inference-efficee solution. These findings val- idate the effectiveness of explicit reasoning in prompt construction for closing the semantic- visual alignment gap in tonagee- generation, without relying on larger language models or expensive optimization at inference time.
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User Promptt P: phoso of hot suliease
Reasoning could be any type, should be large euough contrast the bot dog. highlight these itemss
T21 RePrompt RM
Reward
clearly against neutral baciiground...
RePrompt
Enhanced Prompt r":
T21 Model
ImsgeReward VLM- wwc.eooeeeo
ploto of bot dog, wrupped a wragper, positioned left large, light sand-colored bookgroamd
Structure Reward
Length Reward
+44
GRPO
Figure 2: Overview of the proposed RePrompt. For each input prompt, RePrompt generates multiple reasoning trace and enhanced prompt pairs. The reasoning trace guides the model to produce more detailed, image-grounded prompts. These are used to synthesize candidate images via T2I model, which are then scored by a reward model. Feedback is used to update RePrompt via GRPO.
RELATED WORK
Text to Image Generation. Recently, large-scale diffusion models Labs (2024); Esser et al. (2024) )
Podell et al. (2023); Chen et al. (2024b); Xie et al. (2025); Liu et al. (2024a); Betker et al. (2023)
Ma et al. (2025a); Rombach ct al. (2022); Saharia al. (2022); Zhang al. (2025c have achieved impressive progress in generating high-resolution, photorealistic images from complex textual prompts. To enhance alignment between text and visuals, prior work has explored prompt engineering Hao et al. (023); ;; Mo et al. (2024); : Yeh et al. (2024); Mañas et al. (2024); Yun et al. (025); Cao al. (2023)
): Qin et al. (2024), Yang al. (2024d); Wu at. (2024); Wang al. al.. (2024). often relying on manual or heuristic strategies with limited generalization. Some methods Liu et al. (2024b;; He et al. (2024) utilize iterative feedback or black-box optimization refine prompts over multiple steps. We propose a reinforcement learning based framework that automatically refines prompts through iterative reasoning, achieving better semantic alignment than static or rule-based methods.
Reinforcement Learning. Reinforcement learning (RL) has proven effective in scenarios where iterative feedback is essential for task optimization. In the realm of generative models. RL-based
approaches Guo al.] (2025a); WWlllcce al. (2024); Yang al.] (2024c); Guo et al. (2024): Gupta
et al. (2025); Lee et al. (2024); Zhao et al. (2024); Nabati et al. (2024); Black et al. 2023 Liang
et al. 2024); Kirstain et al. (2023);
3 Lee et al. (2023): Shen et al. (2025): ) Zhang et al. (2024): Wang
et al. (2025a): Jiang al. (2025); Xue et al. (2025) have been employed
fine-tune outputs by
maximizing reward function that encapsulates desired attributes such as image realism, diversity, and semantic fidelity. In our framework, adopt RI techniques to drive the automatic optimization of text prompts. By defining a multi-faceted reward that not only evaluates the visual quality of the generated image but also the interpretability and relevance of the prompt, our approach enables the model to learn an optimal prompt refinement strategy over successive iterations. Notably, T21- RI Diang et al. (025 is closely related our work, but it targets Janus-Pro Chen al. (2025), unified vision- lanion-language model. In contrast, our method trains an auxiliary
LLM that generalizes
across diverse text-to-image models, offering a more flexible Ma et al. (2023 2024 and model- agnostic solution.
Reasoning in LLM. Reasoning in L.MMs Wei et al. (2022) improves complex task solving by de- composing problems into intermediate steps Feng et al. (2025): Huang et al. (2025b); Yang et al.
2025 ; Zhang et al. (2025a); Huang et al. (202aa); ; Yu et al. (2025); Ma et al. (2025b); ; Li et al. 2025 Lu et al. (2025). In multimodal generation, reasoning mechanisms Yang et al. (2024b); Guo et al. (2055)); Wang et al. (025c):; Zhang et al. (025d); Sahili et al. (024)): Chen et al. (2024a) enhance prompt understanding and semantic alignment. Our method integrates reasoning with rein- forcement learning, enabling step-wise prompt refinement that improves text-image alignment, and image muality-offeriin new perspective for prompt optimization in T2I generation.
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3 METHOD
We present RePrompt, a aaasonnnngaugmmnteed reprompting framework for text-to-image (T2I) gen- eration. RePrompt decouples prompt generation from image generation, i.c., training a language model to produce structured, semantically rich prompts, while keeping the T2I backbone fixed. We optimize RePPrompt via reinforcement learning (RL) to directly improve downstream image quality, compositional correctness, and usability.
3.1 FRAMEWORK OVERVIEW
RePrompt comprises three main modules (Figure 2 1) Prompting Policy ne, which produces reasoning trace H and an enhanced prompt P'; 2) a fixed T2I Synthesizer fe. which renders an image I from P': 3) T2I RePrompt Reward Model Rootal P'), which scores the image realism, semantic alignment, and prompt structure.
Given input prompt P, the policy samples: y = P') no (P), the synthesizer then generates: T = (P').. Since fo is non-differentiable, we formulate prompt generation as a single-step Markov Decision Process (MDP): State: the original prompt
Action: sampling y P") нe (y P). Transition: deterministic mapping P' + I fo(P"). Reward r: the reasoning reward
r = Rooaal P*). Objective: maximize Ep-Dl Egnnotul))
We train Te via reinforcement learning (Group Relative Policy Optimization, GRPO Guo et al. 2025a)) to improve downstream image quality. By keeping the T2I model fo fixed, RePrompt leamss backbone-speeific reasoning strategies that enhance semantic fidelity and visual realism with- out requiring any manually annotated reasoning traces.
3.2 T21 REPROMPT REWARD MODEL
central component of our framework is the T2I RePrompt Reward Model-an ensemble, image- grounded reward function specifically designed for
Prompt: "a photo of a hot dog and suitcase"
Tex--inag alignment Image quality? ?
the prompt refinement task in T2I generation. In contrast to generic reward functions used in nat-
From buman perspective:
ural language vision tasks, reward model co-developed with the objective of enhancing reasoning driven prompt construction. It explicitly evaluates whether a generated prompt yields an im- age that is realistic, semantically faithful to the user intent, and compositionally coherent.
Suitease is too small
ImageReward
From robot perspective:
Including both objects
VI.M-Reward 0.7
Figure The ReauallReasoning Reward.
This reward framework critical training Re- Prompt effectively and serves three key goals: (1)
Stable optimization: Each component provides dense and structured feedback, mitigating the chal- lenges of sparse or noisy reward signals during early-stage learning. (2) Multi-faceted supervision: The reward captures complementary aspects of T2I quality, including human preference, visual quality, and semantic alignment, ensuring holistic prompt improvement. (3) Cross-model general- ization: Because the reward depends only on the prompt-image output pair and not on any specific T2I architecture, it generalizes naturally across different generation backbones and unseen prompt distributions. Together, these properties enable our reward model to not only guide the training of the reprompting policy but also ensure broad applicability and stable learning across varying settings in sext-to-image generation.
Tisual-Reasoning Reward (Rvis). This component (as shown in Figure captures both user- aligned preferences and semantic correctness at the image level:
Rvis = a R IMM pref
+ + RVV.M.
Here, RIGG is derived from ImageReward Xu al. (2023), which used to evaluate whether generated images align with human preferences. RYTM is obtained from VLM-Reward Achiam et al. 2003),, which evaluates semantic consistency and visual quality using Aision-language model. The weights and y allow control the trade-off between perceived image quality and factual alignment.
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Structure Reward (Rstrue). To ensure that the generated output maintains clear reasoning prompt format, we enforce structured syntax:
<reason>... ... w/t.raasncporoppt............... ..t.......pppppppp.....
/prompp>>>
We apply a binary reward:
+1,
if format is correct
Ratrue 11 otherwise
(2)
This reward encourages models adhere standardized output layout, simplifying downstream parsing and ensuring the interpretability of reasoning traces.
Length Reward (Ren)). To ensure compatibility with real-world T2I models such as SDXI and FLUX.1, which impose token-length limits, we apply a constraint on prompt length:
+1, Lmin S L S Liaxxy
Rien
(3)
11, otherwise
where the token length of Pl, and [4 [mmax] empirically set to and 77 tokens. This ensures the prompt is both concise and informative.
Ensemble Reward and Optimization. All reward components are normalized to unit variance and summed:
Reotal = Rivia + Ratruc + Ryen-
The RePrompt policy is trained to maximize expected downstream reward:
f* arg max EpwD, [Rotaa P,,
(5)
0
where 7 = fo' and fo is fixed text-to-image generator.
Generalization and Flexibility. Since the reward depends only on the input-output behavior of the system, not the internals of the image model, framework generalizes well across unseen prompts and new T2I backbones. This modularity also enables RePrompt to adapt to model-specific strengths and failure patterns, while maintaining stable and interpretable training signals throughout.
3.3 REPROMPT OPTIMIZATION
Prompt generation for T2I involves a non-differentiable, black-box image renderer fa- RL allows us optimize iTy directly with respect to downstream image outcomes rather than proxy losses on Moreover, because RePrompt is individualized to each T2I backbone, it can adapt its reasoning and prompt style to the specific strengths and limitations of given model improvin generalization and image fidelity without retraining fg.
At each update step, for a given user prompt P, we sample set of G candidate outputs {us ) Teaa (y P). Each candidate y4 (H, P') yields an image I, (P!) and receives a scalar reward r; = Ruotal P. PP). We then compute normalized advantages:
Ti H4 A;
G
T3
(rs 1.)
dr
We use Group Relative Policy Optimization (GRPO) Guo et al. (2025a) as practical and stable update method for training the reprompting policy based on group-wise reward comparisons. The objective is defined as:
(onro(!) Er.(y:
min ps A., clip(p., 1 -
7
BKL KL(TTol P) Teer( P))
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Ka(u, IP) (P) *aasa ()) E and SKL are clipping and penalty coefficients, Tret is reference policy (e.g.. the initial or distillation policy). By fixing fe and optimizing only тe. RePrompt can be applied universally any off-the-shelf model, learning backbone, reasoning and prompt strategies without retraining the image generator. We further validate RePrompt with a variance- reduction analysis, showing that structured reasoning reduces reward uncertainty and lowers the sample complexity for accurate estimation. This leads to faster and more stable GRPO training. Full analysis and proof are provided in Appendix
where P;
4 EXPERIMENTS
4.1 SETTINGS
Implementation Details. We use Qwen2.5-3B Yang et al. (2024a) as our base language model. For the text-to-image model used for training, generates images at a resolution of 512x512 pixels. Our model is trained using the TRL rein- forcement learning framework for 3 epochs, with 4 outputs generated per instance, the weight of ImageReward Xu et al. (202)) and VLM-Reward are both 0.5. The VLM used for computing VL.M- Reward GPT-4V. All experiments were conducted on NVIDIA A100 (80GB) GPUs, and the entire training process required about 6 hours. More details are in the Appendix C
the FLUX.II-eW Labs (2024) model, which
Training Data. Inspired by the prompt construction strategy in GenEval Ghosh et 2023 we adapt six object-centric templates to newly curated list of 288 common daily objects generated via GPT-4[Achian et al. (2023). This results in a training corpus of 9,000 prompts, carefully filtered to avoid overlap with the GenEval. We use 8,000 prompts to fine-tune our RePrompt via supervised learning, and 1,000 prompts for reinforcement learning.
Evaluation Setup. To assess RePrompt, we evaluate two benchmarks: GenEval Ghosh et al. (203) and T21-Compbench Huang et al. (2023). GenEval focuses on instance-level alignment with user intent using concise prompts, while T2I-Compeench measures compositional generation under complex scenarios involving multiple objects, attributes, and spatial relations.
4.2 COMPARISION
Table I presents the performance comparison across various text-to-image generation models eval- uated the GenEval benchmark, which assesses six fine-graineds composition capabilities: single- object, two-object, counting, color, spatial position, and attribute binding.
Notably, our method demonstrates exceptional gains in spatial layout understanding (Position). For instance, when built upon FLUX Labs) (2024) our approach achieves 0.62 score on Position, rep- resenting a +77.1% relative improvement over the Qwen2.5 3B baseline. Similarly, for SD3 Esser et al. (2024), we observe a +78.8% gain, and for Pixart-E Chen et al.) (2024b). the relative im- provement reaches an impressive +122.2%. This strong enhancement highlights the strength of our compositional training strategy in explicitly grounding spatial relations between objects. Beyond Position metric, our method also achieves substantial improvements in Counting (+22.2% for FLUX, +13.2% for SD3, +16.7% for Pixart-E) and moderate gains in attribute binding (+2.1% to +9.4%) As a result, we observe consistent boosts in overall GenEval scores: + +11.8% (FLUX), +10.3% (SD3), and +6.9% (Pixart-E). These results verify the effectiveness of reinforcement learning- based reprompting strategy, which enables the language model to iteratively reason about visual composition and generate more precise, image-aligned prompts, and brings state-of- oftte--f--hearrr advances in spatial understanding.
4.3 GENERALIZATION PERFORMANCE ON T21-COMBBENCH
We further assess the robustness of our method on the T21-Compbench benchmark, which tests compositional generalization across six dimensions: color, shape, texture, spatial reasoning. nu- meracy, and complex attribute combinations. As shown in Table 2 RePrompt consistently im- proves performance across all evaluated backbones. In particular, significantly boosts spatial

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Table 1: Evaluation of text-to-image generation the GenEval benchmark. Our method consis-
tently outperforms strong baselines, achieving the best overall scores. Notably, our approach shows substantial gains in spatial position understanding over Qwen2.5 3B-enhanced baselines, demon- strating its superior capability in grounding spatial relations.
Method
Single object
Counting
Colors
Position
Attribute binding
Overall
object
FLUX Labs (2024)
0.99
0.79
0.75
0.78
0.18
0.45
+ Promptist Hao el al. 2023
0.98
0.72
0.70
0.78
0.21
0.44
0.66
+ PAG Yun et al. 2025
0.97
0.74
0.73
0.80
0,36
0,46
0.69
GPT4
0.99
0.79
0.68
0.84
0.51
0.52
0.72
GPTS
1.00
0.81
0.70
0.85
0.51
0.51
0.73
+ GPTo3
0.76
0.65
0.83
0.48
0.50
+ Deepseek-rl
1.00
0.81
0.56
0.78
0.47
0,43
0.67
+ Qwen2.5 7B
0.99
0.83
0.62
0.84
0.36
0.51
0.69
Qwen2.5 3B
0.99
0.84
0.63
0.81
0.35
0.48
0.68
+ Ours (train FLUX)
0,98
0.77
0.85
0.62
0.49
0.76
Improvement over Qwen2.5 3B
-1.0%
+3.6%
+22.2%
+4.9%
+77.1%
+2.1%
+1 +11.8%
SD3 Esser et al. 2024)
1.00
0.85
0.62
0.88
0.22
0.58
0.69
+ Promptist
0.99
0.84
0.66
0.84
0.45
0.52
0.69
+ PAG
0.99
0.85
0.68
0.85
0.49
0.53
0.71
GPT4
1.00
0.84
0.51
0.85
0.48
0.54
0.70
+ GPTS
0.85
0.53
0.85
0.47
0.50
0.70
+ GPTo3
0,99
0.82
0.50
0.84
0,41
0.52
0.68
+ Deepseek-rl
0,99
0.82
0.53
0.80
0.44
0,46
0.67
Qwen2.5 7B
1,00
0.82
0.49
0.85
0.34
0.58
0.68
+ Qwen2.5 3B
1.00
0.86
0.53
0.84
0.33
0.55
0.68
+ Ours (train w/ FLUX)
0.99
0.86
0.60
0.86
0.59
0.60
0.75
Improvement over Qwen2.5 3B
-1.0%
0.0%
+33.2%
+2.4%
+78.8%
+9.1%
+ +10.3%
Pixart-S Σ Chen et al.
2024b
0.99
0.60
0.47
0.81
0.10
0,26
0.54
+ Promptist
0.98
0.60
0.49
0.80
0.20
0.27
0.55
PAG
0.98
0.63
0.52
0.80
0.28
0.29
0.56
GPT4
0,96
0.48
0.84
0.36
0.31
+ GPTS
0.97
0.68
0.49
0.84
0.37
0.31
0.61
+ GPTo3
0.96
0.67
0.48
0.83
0.35
0.31
0.60
+ Deepscek-rl
0.99
0.63
0.43
0.78
0.24
0.27
0.56
Qwen2.5 7B
0.96
0.67
0.43
0.83
0.20
0.32
0.57
+ Qwen2.5
0.82
0.18
0.32
+ Ours (train w/ FLUX)
0.98
0.64
0.56
0.81
0.40
0.35
0.62
Improvement over Qwen2.5 3B
-1.0%
5.99%
+16.7%
-1.2%
+122.2%
79.4%
+6.9%
Table 2: Eyaluation of text-to-image generation on the 121-Comphench. We report the baseline
results, their variants enhanced with Qwen2.5 3B, and our method trained with FLUX. Our approach consistently improves performance across most aspects, particularly in Spatial compositions.
Method
Color
Shape
Texture
Spatial
Numeracy
Complex
FLUX
0.7223
0.4796
0.6522
0.2494
0.6101
0.3616
Qwen2.5 3B Ours (train w/ FLUX)
0.7149
0.5103
0.6012
0.2579
0,5982
0.3325
0.7501
0.5276
0.6515
0.3301
0.6499
0.3721
SD3
0.7941
0,5812
0.7224
0.2815
0,5871
0.3714
+ Qwen2.5 3B
0.7227
0.5478
0.6581
0.2549
0,5934
0,3307
Ours (train w/ FLUX)
0.7866
0.5891
0.7184
0.3315
0.6289
0.3744
Pixart-Y
0,5682
0.4717
0.5622
0.2497
0,5366
0,3655
Qwen2.5 3B
0.6618
0.4814 14
0.5662
0.2481
0,5443
0.3335
Ours (train w/ FLUX)
0.6665
0.5011
0.6190
0.2913
0.5716
0.3680
compositional scores (e.g., from 0.2494 to 0.3301 FLUX and from 0.2815 to 0.3315 on SD3) and enhances numeracy understanding, two long-standing challenges in text-to-image generation. Moreover, our method outperforms stronger I.LMMenhanced baselines, for instance, Pixart-E,,

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Table 3: Ablation study of SFT and RL the GenEval benchmark.
Method
Single object
Two object
Counting
Colors
Position
Attribute binding
Overall f
FLUX Labs 2024)
0.99
0.79
0.75
0.78
0.18
0.45
0.66
+ Qwen2.5 3B
0.99
0.84
0.63
0.81
0.35
0.48
0.68
w/ SFT
0.99
0.83
0.64
0.81
0.43
0,44
0.69
w/ RL
0.98
0.83
0.71
0.87
0.41
0.53
0.72
SFT + RI.
0.98
0.87
0.77
0.85
0.62
0.49
0.76
Table 4: Quantitative comparison between RePrompt and other method on image generation accu- racy and latency with the subset Geneval. All latency measured on single NVIDIA A100 GPU.
Method
Accuracy 1
Latency (s img) 1
FLUXX
0.65
20
Show-o
Xic et al. 2024)
0.55
3
Idea2Img Yang et al.
2024d
(w/ FLUX)
0.69
140
PARM++ Guo ot al.
2025b
(w/ Show-o)
0.72
110
RePrompt (w/ FLUX)
0.76
30
RePrompt achieves notable gains in both texture and spatial dimensions. These results demonstrate that our approach generalizes well across diverse models and compositional skills, validating its effectiveness as a versatile and plug-and-play enhancement for real-world T2I systems.
4.4 ABLATION STUDY
Ablation on SFT and RL. Table shows the effect of supervised fine-tuning (SFT) and reinforce- ment learning (RL) on GenEval. SFT brings modest gains (+0.01 overall), mainly improving posi- tional understanding (0.35-00.43), suggesting it helps inject object-attribute knowledge but strug- gles with complex reasoning. RL yields larger boosts (+0.04 overall), as it directly optimizes visual correctness. Combining SFT and RL achieves the best results (0.76 overall), with strong improve- ments in spatial reasoning (0.62) and counting (0.77). These results confirm that SFT offers useful priors, while RL is key for compositional robustness.
Ablation Study on Reasoning. Table 5 presents an ablation study on the GenEval benchmark to assess the impact of reasoning in our reinforcement learning framework. The FLUX baseline, inte- grating a large language model (+Qwen2.5 3B) brings modest gains across most categories, raising the overall score to 0.68. Applying RL without reasoning achieves a similar overall improvement (0.68), suggesting that reward-driven optimization alone contributes to better alignment. However, incorporating reasoning into the RL loop leads to more substantial improvement, pushing the over- all score to 0.72. Notably, categories that require more complex semantic understanding-Suun "Colors" (from 0.83 to 0.87) and " "Attribute binding" (from 0.46 to benei- the most. These results demonstrate that step-by-step reasoming helps the model better decompose and interpret tex- tual prompts, thereby enabling more accurate and faithful image generation.
as
Comparison on Accuracy and Latency. Table benchmarks our RePrompt against prior methods in terms of both generation accuracy (GenEval overall) and inference latency. Among base models, FLUX.1 achieves higher accuracy compared to Show-o but suffers from a longer latency (20s vs. 38 per image), reflecting trade-off between quality and speed. When incorporating advanced prompt- ing techniques, Idea2Img (w/ FI.UX.1) 1) improves accuracy to 0.69 but at the cost of a significant latency increase (140s per image), while PARM++ (w/ Show-o) achieves 0.72 accuracy with 10s latency. In contrast, RePrompt achieves the best accuracy while maintaining much lower lateney. This demonstrates the effectiveness of RePrompt in enabling precise visual grounding without com- promising efficiency, making it more practical for real-world deployment.
Ablation Study on Trained T2I Models. Figure Illustrates the generalization ability of RePrompt across three diverse T2I backbones: FLUX, SD3, and PixArt-E. Our method consistently boosts compositional scores on GemEval-imppoo FLUX from 0.66 to 0.76, SD3 from 0.69 to 0.75, and Art- from 0.54 to 0.62 outperorrnni both baselines and the +Qwen2.5 variants. These
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Table 5: Ablation study of reasoning on the GenEval benchmark.
Method
Single object
Two object
Counting
Colors
Position
Attribute binding
Overall 1
FLUX
0,99
0.79
0.75
0.78
0.18
0,45
0,66
+wwen2.5 3B
0.99
0.84
0.63
0.81
0.35
0.48
0.68
RL w/o reasoning
1.00
0.81
0.68
0.83
0.33
0,46
0,68
RL w/ reasoning
0.98
0.83
0.71
0.87
0.41
0.53
0.72
Baseling
+Qwen2.5
+Ours((/FLLUX
+Ours(w/SD3)
+Ours(w/Pixart
0.76
0.75
0.73
0.72
0.71
0.70
0.68
0.69
0.68
0.62 0.62
0.66
0.61
0.54
FLUX
SD3
Pixart-Z
Figure 4:
Impact of our method across different base T2I models on the GenEval benchmark. Our method consistently improves the compositional understanding across all base models.
results demonstrate RePrompt's robustness and model-agnostic design, working effectively across both large (e.g., SD3) and lightweight (e.g.. PixArt-E) backbones. Notably, observe that stronger T2I models used during training lead to better generalization at inference, likely due to richer rea- soning patterns induced during learning.
Qualitative Comparison. Figure 5 illustrates the qualitative advantage RePrompt over existing T2I models. Baseline models often generate images with incorrect spatial relations hallucinated objects. For example, when prompted with "a fire hydrant with a tennis racket," DALL-E 3 pro- duces unrealistie, stylistic blends where the objects grounds the tennis racket "around the base" of the fire hydrant, respecting the intended composi- tion. Similarly, for "a photo of a dog above cow," our method correctly depicts the dog in the air above the cow with "a high angle shot", aligning with the prompt semantics. These results high- light RePrompt's ability mitigate typical failures in spatial arrangement and object interaction by generating prompts with explicit compositional cues. More cases are in the Appendix F
merged. In contrast, RePrompt accurately
More Ablation Study. We present additional ablation studies on training dynamics, and reward functions in the Appendix(Djo further validate the effectiveness of our reasoning design and reward selection, as well as the stability of the training process.
5 CONCLUSION
In this work, we propose simple yet effective method to enhance compositional understanding in text-to-image (T2I) generation models. By injecting chain-of-thouge (CoT) reasoning into the prompt construction pipeline and pairing each CoT with an enhanced prompt, our approach im- proves the alignment between textual descriptions and generated images. Extensive experiments on the GenEval benchmark demonstrate that our method consistently improves performance across various T2I backbones, including FLUX. SD3, and Pixart-E, without requiring additional model retraining. These results highlight the generalizability and plug-and and-play nature of method. We believe our approach offers a new perspective for improving controllability and compositional fidelity in generative models.

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The dog should be Published as a conference paper at ICLR 2026
User prompt
DELL-E3
Reasoning
Ours
Let's break down where the tennis racket is positioned around the base the fire hydrant
Prompt: fire hydrant with tennis racket"
break down sandwich should be the main focus, but the scissor should add context, the action of cutting...
Prompt: "a pair of scissors and sandwich"
Let's break down
positioned above the cow, which suggests a bird's- eye view or a high angle shot. The dog the air above the cow
Prompt: photo of dog above cow"
Figure 5: Qualitative results compositional prompts. Compared to vanilla T2I models, our Re- Prompt improves spatial layout and object relations by generating enhanced prompts with explicit reasoning, leading to more faithful compositions.
ACKNOWLEDGMENTS
This work was supported by the National Key Research and Development Program of China (No. 2025YFE01 13500), the National Science Fund for Distinguished Young Scholars (No.62025603), the National Natural Science Foundation of China (No. U22B2051, No. 62302411), and the Zhong- guancun Academy, Beijing, China (No. 20240103).
ETHICS STATEMENT
adhere to the ICLR Code Ethics. This work does not involve human subjects, sensitive data, or harmful applications. Dataset usage complies with all relevant licenses. The authors declare no conflicts interest. We are committed fairness, privacy, and security, and confirm that ethical standards and institutional requirements have been met.
REPRODUCIBILITY STATEMENT
We have taken several steps to ensure the reproducibility of our results. Detailed descriptions of the model architecture, training procedures, and experimental settings are provided the main text and supplementary materials. All assumptions and derivations underlying our theoretical claims are clearly explained and fully proven in the appendix. The datasets used in our experiments are provide anonymized source code supplementary material
publicly available. Additionally, to facilitate replication of our experiments. We encourage readers to refer to these materials for complete understanding of our methodology and experimental setup.
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REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, lige Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, al. Gpt-4 technical report. arXiv preprint S3K3::2300.087771 2023.
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang. Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science. https://cdn. openai. comfpapers/dall--.. pdf. 2(3):8, 2023.
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning. arXiv preprint arXiv:2305. 13301, 2023.
Tingfeng Cao, Chengyu Wang, Bingyan Liu, Ziheng Wu, Jinhui Zhu, and Jun Huang. Beauti- fulprompt: Towards automatic prompt engineering for text-to-image synthesis. arXiv preprint arXiv:2311.06752, 2023.
Anthony Chen, Jianjin Xu, Wenzhao Zheng, Gaole Dai, Yida Wang, Renrui Zhang, Haofan Wang, and Shanghang Zhang. Training feree free regional prompting for diffusion transformers. arXiv
preprint (i.ii:2411.0239900 2024a.
Junsong Chen, Chongjian Ge, Enze Xie, Yue Wu, Lewei Yao, Xiaozhe Ren, Zhongdao Wang, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-o:: Weak-to-strone so-k-n training of diffusion transformer for 4k text-to-image generation. In European Conference on Computer Vision, pp. 74-91. Springer, 2024b.
Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling, arXiv preprint (xXxx:2501.78/11 2025.
Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Wei- hao Yu, Xiaonan Nie, Ziang Song. Guang Shi, and Haoqi Fan. Emerging properties in unified multimodal pretraining. arXiv preprint arXiv:2505. 2025.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. Forty-first international conference on machine learning, 2024.
Kaituo Feng, Kaixiong Gong, Bohao Li, Zonghao Guo, Yibing Wang, Tianshuo Peng, Benyou Wang, and Xiangyu Yue. Video-rl: Reinforcing video reasoning in mllms. arXiv preprint arXiv:2503.21776. 2025.
Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. Advances Neural Information Processing Systems, 36: 52132-52152, 2023.
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arxiv:2501.12944 2025a.
Jianshu Guo, Wenhao Chai, Jie Deng, Hsiang- -eii Huang, Tian Ye, Yichen Xu, Jiawei Zhang, Jenq-Neng Hwang, and Gaoang Wang. Versat2i: Improving text-to-image models with versatile reward. arXiv preprint arXiv:2403.18493, 2024.
Ziyu Guo, Renrui Zhang, Chengzhuo Tong, Zhizheng Zhao, Peng Gao, Hongsheng Li, and Pheng- Ann Heng, Can generate images with cot? let's verify and reinforce image generation step by step. arXiv preprint arXiv:2501.13926. 2025b.
Shashank Gupta, Chaitanya Ahuja, Tsung- Yu Lin, Sreya Dutta Roy, Harrie Oosterhuis, Maarten de Rijke, and Satya Narayan Shukla. A simple and effective reinforcement learning method for text-to-image diffusion fine-tuning. arXiv preprint arXiv:2503.00897, 2025.

---

Published as a conference paper at ICLR 2026
Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang. Zehuan Yuan, Bingyue Peng, and Xiaobing Liu. Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis, 2024.
htttt:t////// /awxxviivii//./////vv org/ abs 22t..221..00020 04431
Yaru Hao, Zewen Chi, Li Dong, and Furu Wei. Optimizing prompts for soxt-to-image generation. Advances in Neural Information Processing Systems, 56:66923-66939, 2023.
Yutong Hc, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Williams, George Pappas, Hamed Hassani, Yuki Mitsufuji, Ruslan Salakhutdinov, and J Zico Kolter. Automated black-box prompt engineering for personalized text-to-image generation. arXiv preprint arXiv:2403. 19103, 2(5), 2024.
Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation. Advances in Neural Information Processing Systems, 36:78723-787477 2023.
Qihan Huang, Long Chan, Jinlong Liu, Wanggui He, Hao Jiang, Mingli Song, Jingyuan Chen, Chang Yao, and Jie Song. Boosting milm reasoning with text-debiased hint-grpo. arXiv preprint vrXiv:2503.23900 2025a.
Wenxuan Huang, Bohan Jía, Zijie Zhai, Shaosheng Cao. Zheyu Ye, Fei Zhao, Yao Hu, and Shaohui Lin. Vision-rl: Incentivizing reasoning capability in multimodal large language models. arXiv preprint 0rX1: 2025b.
Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. Openai ol system card. arXiv preprint arXiv:2412.16720 2024.
Dongzhi Jiang, Ziyu Guo, Renrui Zhang, Zhuofan Zong, Hao Li, Le Zhuo, Shilin Yan. Pheng-Ann Heng, and Hongsheng Li. T2i-r1: Reinforcing image generation with collaborative semantic-level and loven-level cot. arXiv preprint arXiv:2505.00703, 2025.
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Pick- a-pic: An open dataset of user preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36:36632-36666666 2023.
Black Forest Labs. Flux. https::/ http:///hhh.b..coo/oaateeeeeeeee forestt-forrttt-aaaaa
2024.
Kimin Lee, Hao Liu, Moonkyung Ryu, Olivia Watkins, Yuqing Du, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, and Shixiang Shane Gu. Aligning text-to-image models using human feedback. arXiv preprint arXiv:2300. 12192 2023.
Scung Hyun Lee. Yinxiao Li, Junjie Ke, Innfarn Yoo, Han Zhang, Jiahui Yu, Qifei Wang, Fei Deng, Glenn Entis, Junfeng He, et al. Parrot: Pareto-optimal multi-reward reinforcement learning frame- work for text-to-iman to-image generation. In European Conference on Computer Vision, pp. 472-478. Springer, 2024.
Xinhao Li, Ziang Yan, Desen Meng, Lu Dong, Xiangyu Zeng. Yinan He, Yali Wang, Yu Qiao, Yi Wang, and Limin Wang. Videochat-rl: Enhaneing spatio-temporal perception via reinforce- ment fine-tuning, arXiv preprint arXiv:2504.06958. 2025.
Youwei Liang, Junfeng He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, et Rich human feedback for text-to-image genera- tion. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 19401-19411, 2024.
Bingchen Liu, Ehsan Akhgari, Alexander Visheratin, Aleks Kamko, Linmiao Xu, Shivam Shrirao, Chase Lambert, Joao Souza, Suhail Doshi, and Daiqing Li. Playground v3: Improving text- to-image alignment with decp-fusion large language models. arXiv preprint arXiv:2409. 10695, 2024a.
Shihong Liu, Samuel Yu, Zhiqiu Lin, Deepak Pathak, and Deva Ramanan. Language models as black-box optimizers for vision-language models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 12687-12697, 2024b.
12

---

Published as a conference paper at ICLR 2026
Zhengxi Lu, Yuxiang Chai, Yaxuan Guo, Xi Yin, Liang Liu, Hao Wang, Guanjing Xiong, and Hongsheng Li. Ui-rl: Enhancing action prediction gui agents by reinforcement learning. arXiv preprint arXiv:2503.21622 2025.
Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, et al. Inference- time scaling for diffusion models beyond 2025a.
scaling denoising steps. arXiv preprint arXiv:2501.0977
Xinyu Ma, Ziyang Ding, Zhicong Luo, Chi Chen, Zonghao Guo, Derek F Wong, Xiaoyi Feng, and Maosong Sun. Deepperception: Advancing rl-like cognitive visual perception in mlims for Knowledge-intensive visual grounding. arXiv preprint arXiv:2503.12799 2025b.
Yuexiao Ma, Taisong Jin, Xiawu Zheng, Yan Wang, Huixia Li, Yongjian Wu, Guannan Jiang, Wei Zhang, and Rongrong Ji. Ompq: Orthogonal mixed precision quantization. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp. 9029-9037, 2023.
Yuexiao Ma, Huixia Li, Xiawu Zheng, Feng Ling, Xuefeng Xiao, Rui Wang, Shilei Wen, Fei Chao, and Rongrong Ji. Affinequant: Affine transformation quantization for large language models. arXiv preprint arXiv:2403.12544 2024.
Oscar Mañas, Pietro Astolfi Melissa Hall, Candace Ross, Jack Urbanek, Adina Williams, Aish- warya Agrawal, Adriana Romero-Soriano, and Michal Drozdzal. Improving text-to-image con- sistency via automatic prompt optimization. arXiv preprint (xXiv:2403.17800 2024.
Wenyi Mo, Tianyu Zhang, Yalong Bai, Bing Su, Ji-Rong Wen, and Qing Yang. Dynamic prompt op- timizing for text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 26627-26636, 2024.
Ofir Nabati, Guy Tennenholtz, ChihWei Hsu, Moonkyung Ryu, Deepak Ramachandran, Yinlam Chow, Xiang Li, and Craig Boutilier. Personalized and sequential text-to-image generation. arXiv preprint ar Aixiv:2412. 10419, 2024.
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.0/992, 2023.
Jie Qin, Jie Wu, Weifeng Chen, Yuxi Ren, Huixia Li, Hefeng Wu, Xuefeng Xiao, Rui Wang, and Shilei Wen. Diffusiongpt: Llm-driven text-to-image generation system. arXiv preprint arXiv:2401. 10061, 2024.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and puttern recognition, pp. 10684-10695, 2022.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural informa- tion processing systems, 35:36479-364999 2022.
Zahraa Al Sahili, Ioannis Patras, and Matthew Purver. Faircot: Enhancing fairness in diffu- sion models via chain of thought reasoning of multimodal language models. arXiv preprint 1xXiv:2406.090700 2024.
Haozhan Shen, Peng Liu, Jingcheng Li, Chunxin Fang, Yibo Ma, Jiajia Liao, Qiaoli Shen, Zilun Zhang, Kangjia Zhao, Qianqian Zhang, et al. Vlm-rl: A stable and generalizable rl-style large vision-language model. arXiv preprint arXiv:2504. 07611,, 2025.
Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Chonghua Liao, et al. Kimi k1. 5: Scaling reinforcement learning with llms. arXiv preprint arXiv:2501. 12599, 2025.
Luong Trung, Xinbo Zhang, Zhanming Jie. Peng Sun, Xiaoran Jin, and Hang Li. Reft: Reasoning with reinforced fine-tuning. In Proceedings of the 62nd Anmual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), pp. 7601-7614, 2024.
13

---

Published as a conference paper at ICLR 2026
Bram Wallace, Meihua Dang. Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. Diffusion model alignment using direct preference optimization. In Proceedings of the IEEE/CVF Conference Computer Viston and Puttern Recognition, pp. 8228-8238, 2024.
Junke Wang. Zhi Tian, Xun Wang, Xinyu Zhang, Weilin Huang, Zuxuan Wu, and Yu-Gang Jiang. Simplear: Pushing the frontier of autoregressive visual generation through pretraining, sft, and rl. arXiv preprint arXiv:2504. /455, 2025a.
Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, al. Internv13. 5: Advancing open-source multimodal models in versatility, reasoning, and efficiency. arXiv preprint arXiv:2508. 18265, 2025b.
Yi Wang, Mushui Liu, Wanggui He, Longxiang Zhang, Ziwei Huang, Guanghao Zhang, Fangxun Shu, Zhong Tao, Dong She, Zhelun Yu, et al. Mint: Multi-modal chain thought in unified generative models for enhanced image generation. arXiv preprint arXiv:2503.01298, 2025c.
Zhenyu Wang, Aoxue Li, Zhenguo Li, and Xihui Liu. Genartist: Multimodal Ilm as an agent for unified image generation and editing. Advances in Neural Information Processing Systems, 37: 128374-128395. 2024.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837, 2022.
Xinyu Wei, Jinrui Zhang, Zeqing Wang, Hongyang Wei, Zhen Guo, and Lei Zhang. Tiif-bench: How does your t2i model follow your instructions? arXiv preprint 1xXi:25066002611 2025.
Tsung-Har Wu, Long Lian, Joseph E Gonzalez, Boyi Li, and Trevor Darrell. Self-correcting 11m- controlled diffusion models. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 6327-632 6336, 2024.
Enze Xie, Junsong Chen, Yuyang Zhao, Jincheng Yu, Ligeng Zhu, Yujun Lin, Zhekai Zhang, Muyang Li, Junyu Chen, Han Cai, et al. Sana 1.3: Efficient scaling of training- time and inference- time compute in linear diffusion transformer. arXiv preprint ar arXhv:2501.18427 2025.
Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang. Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. arXiv preprint arXiv:2408, /2528, 2024.
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36: 16003-1555955 2023.
Zeyue Xue, Jie Wu, Yu Gao, Fangyuan Kong, Lingting Zhu, Mengzhao Chen, Zhiheng Liu, Wei Liu, Qiushan Guo, Weilin Huang, et al. Dancegrpo: Unleashing grpo on visual generation. arXiv preprint arXiv:2505.029 078808 2025.
An Yang, Baosong Yang. Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. 5 technical report. arXiv preprint 242.:2412..1115: 2024a.
Ling Yang, Zhaochen Yu, Chenlin Meng, Minkai Xu, Stefano Enmon, and Bin Cui. Mastering text- to-image diffusion: Recaptioning, planning, and generating with multimodal llss. In Forty-first International Conference on Machine Learning, 2024b.
Shentao Yang, Tianqi Chen, and Mingyuan Zhou. A dense reward view on aligning text-to-image diffusion with preference. arXiv preprint arXiv:2402.0826, 2024c.
Yi Yang, Xiaoxuan He, Hongkun Pan, Xiyan Jiang, Yan Deng, Xingtao Yang, Haoyu Lu, Dacheng Yin, Fengyun Rao, Minfeng Zhu, et al. R1-onevision: Advancing generalized multimodal rea- soning through cross-modal formalization. arXiv preprint arXiv:2503. N.X6:2503..0665. 2025.
14

---

Published as a conference paper at ICLR 2026
Zhengyuan Yang. Jianfeng Wang, Linjie Li, Kevin Lin, Chung Ching Lin, Zicheng Liu, and Lijuan Wang. Idea2img: Iterative self-refinement with gpt-4v for automatic image design and generation. In European Conference Computer Vision, pp. 167-184. Springer. 2024d.
Shih- Shih-Ying Yeh, Sang-Hyun Park, Giyeong Oh, Min Song, and Youngjae Yu. Tipo: Text image with text presampling for prompt optimization. arXiv preprini urXiv:2411.08122 2024.
En Yu, Kangheng Lin, Liang Zhao, Jisheng Yin, Yana Wei, Yuang Peng, Haoran Wei, Jianjian Sun, Chunrui Han, Zheng Ge, al. Perception-rl: Pioneering perception policy with reinforcement learning. arXiv preprint arXiv:2504.07954. 2025.
Taeyoung Yun, Dinghuai Zhang, Jinkyoo Park, and Ling Pan. Learning to sample effective and diverse prompts for text-to-image generation. arXiv preprint 1477 2025.
Jingyi Zhang, Jiaxing Huang. Huanjin Yao. Shunyu Liu, Xikun Zhang, Shijian Lu, and Dacheng Tao. R1-vl: Learning to reason with multimodal large language models via step-wise group relative policy optimization. arXiv preprint arXiv:2503: 12937, 2025a.
Xinchen Zhang, Ling Yang, Guohao Li, Yaqi Cai, Jiake Xie, Yong Tang, Yujiu Yang, Mengdi Wang, and Bin Cui. Itercomp: Iterative composition-awe aware feedhack learning from model gallery for text-to-image generation. arXiv preprint (rxiv:2410.071110 2024.
Xuying Zhang, Yutong Liu, Yangguang Li, Renrui Zhang, Yufei Liu, Kai Wang, Wanli Ouyang, Zhiwei Xiong, Peng Gao, Qibin Hou, et al. Tar3d: Creating high-quality 3d assets via next-part prediction. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp- 5134-5145, 2025b.
Xuying Zhang, Yupeng Zhou, Kai Wang, Yikai Wang, Zhen Li, Shaohui Jiao, Daquan Zhou, Qibin Hou, and Ming-Ming Cheng. Ar-1-to-3: Single image to consistent object via next-view prediction. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp-
26273-26283. 2025c.
Yuyao Zhang, Jinghao Li, and Yu-Wing Tai. Layercraft: Enhancing text-to-image generation with reasoning and layered object integration. arXiv preprint arXiv:2504.00 00010, 2025d.
Hanyang Zhao, Haoxian Chen, Ji Zhang, David D Yao, and Wenpin Tang. Scores as actions: framework of fine-tumi fine-tuning diffusion models by continuous-ting reinforcement learning. arXiv preprint arXiv:2409, 240.0000,, 2024.
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APPENDIX
A BROADER IMPACT
Our work aims enhance the controllability and fidelity of text-to-image generation by aligning prompt engineering with user intent through self-reflective reasoning. This could have wide range of beneficial applications, such as improving accessibility tools for the visually impaired, assisting designers in rapid prototyping, or helping educators generate custom visual teaching materials. By reducing the gap between user intentions and generated images, our method empowers non-expert users to communicate complex visual ideas more effectively.
However, like many generative technologies, our system also presents potential risks. Enhanced prompts with richer semantics could inadvertently be exploited to generate more realistic harmful or misleading content, especially the context of misinformation or deepfakes. To mitigate such risks, we recommend pairing method with content moderation filters and safety alignment mech- anisms in deployment. Additionally, since our method involves reinforcement learning with learned reward models, any bias in the reward signal may propagate into the prompt generation. Future search should explore fairness-aware training objectives and human-in-the-loop evaluation to ensure responsible use.
Overall, we believe that RePrompt contributes to the development of more interpretable, control- lable, and user-aligned generative models, which are essential for trustworthy Al applications.
B VARIANCE REDUCTION VIA STRUCTURED REASONING
In this appendix, provide full theoretical analysis showing that conditioning prompt generation on an explicit reasoning trace H strictly reduces the variance of the downstream reward estimator: This reduction in variance directly leads to improved sample efficiency for reinforcement learning.
B.
SETUP AND NOTATION
Let:
P be the space of bare prompts P
be the space reasoning traces H.
r ::P + [0, Rmax] the reward random variable obtained by sampling p' xt (P)) and generating an image I = fP')..
TH H. x P + [0, Rmax] be the reward when first sampling H TH (P),,, then pr π (PP H), and finally / fo
All expectations and variances are taken the joint sampling of H and P'.
B.2 LAW OF TOTAL VARIANCE
By the law of total variance,
Var Var(r(P')) = En [arrl((P)) H]] + Varw (E((P') H]]
Since variances are nonnegative, we immediately have: Theorem B.1 (Variance Reduction).
Var ((trr(1 = EH Var[r u1] < Varlr(P')).
Proof. By definition,
Var [rH, = EulWVarle H]]
and since
Varjr(P')) For [Var(r H] + Varw [F(r H]).
the nonnegativity of Varn [F(r H]] yields the result.
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B.3 SAMPLE COMPLEXITY IMPROVEMENT
Variance directly impacts the number of samples required to estimate the expected reward within given accuracy. Consider estimating the expected reward μ E(r] by drawing N independent samples {r.}. By Chebyshev's inequality,
Var[r]
Pr i
26)
Ne²
Thus, to guarantee Pr(lă μ1] 11/26 2) S 6, require
Var[r]
N
228
Applying Theorem B.1 using reasoning reduces the required sample size:
Var[r(H, PM]
Varlr(P')]
N Wrassoning
Noare-
E20
228
B.4 DISCUSSION
This analysis formalizes the intuition that introducing an intermediate reasoning trace H conditions the policy on structured, compositional information about the scene, thereby reducing uncertainty (variance) in the reward signal. Empirically, this translates to fewer rollouts needed during GRPO training and more stable gradient estimates accelerating convergence without additional data or annotations.
C EXPERIMENT SETTING
Table 6 summarizes the key hyperparameters used in our experiments, including configurations for the GRPO optimization algorithm, the FLUX.1 text-to-image model, and the joint training process. For GRPO, we set the clipping range E to 0.2 and the KL penalty coefficient to 0.04 with a group size of 4. The FLUUX.1 model operates classifier-free guidance (CFG) scale 3.5. In joint training, we balance ImageReward and VL.M- Reward with equal weights (0.5 each), and constrain prompt lengths between 15 and 77 tokens. Training is conducted using 8 devices with per-device batch size of 4, learning rate of 2e-6, gradient accumulation steps of 2, and a total of epochs.
resolution of 512 x 512, with 50 diffusion steps and
D ABLATION STUDY
Training Dynamics. Figure presents presents the reward curve during reinforcement learning of the Re- Prompt. We observe a stable and monotonically increasing trend the reward, demonstrating that reward model provides reliable and effective supervision signal throughout training. The ab- sence of sharp fluctuations or reward collapse suggests that our RL setup maintains stable policy updates. This aligns with the observed downstream improvements, confirming that ewaard-guidd prompt refinement effectively enhances compositional alignment in generated images.
Ablation Study Visual Reasoning Rewards. To investigate the effectiveness of different vi- sual reasoning reward signals used in our method, we conduct a detailed ablation study on the GenEval benchmark. As shown in Table our method consistently improves over the FLUX base- line and Qwen2.5 3B variant across all subcategories, demonstrating the efficacy of reward-driven learning in aligning generated images with prompt semantics. Both ImageReward and show noticeable gains over the +Qwen2.5 3B bascline, indicating that each reward captures com- plementary aspects of visual faithfulness. However, their standalone performances are still limited in challenging tasks like Position. Notably, our ensemble reward formulation-while combines both ImwgeReward and VLM-based feedback-achie -achivves the best overall performance, with consis- tent improvements compared to the +Qwen2.5 3B baseline. These precisely the categories that require stronger compositional understanding and spatial reasoning, underscoring the strength of composite reward design in driving semantically aligned image generation. The ablation firms that each reward contributes uniquely, and their integration enables more holistic supervision, leading to substantial gains in compositional image-text alignment.
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Table 6: Key Parameters for GRPO and T2I Model.
Config
Symbol
Value
GRPO Config
Clipping range
0.2
KL penalty coefficient
0.04
Group size
G
FLUX.1 Config
Image resolution
H W
512 x 512
Diffusion steps
T
50
CFG scale
Ade
3.5
Joint Training Parameters
ImageReward weight
0.5
VLM-Reward weight
0.5
Min prompt's length
Lmin
15
Max prompt's length
Tynax
77
Device number
Per device batch size
B
Learning rate
lr
2
Gradient Accumulation
Epoch
Reward
Reward t2i
0.6
Value
0.2
train/reward
reward
100
20
60
100
Global Step
Global Step
Format Reward Func
Reward
1.0
0.5
Value
0.8
0.0
0.6
-0.5
format_ rernat__wward__mmm func
reward len
0
20
40
60
80
100
0
20
40
60
80
100
Global Step
Global Step
Figure 6: Training curve of our RePrompt during reinforcement learning. The curve shows steady reward improvement, indicating stable training dynamics and effective reward signal.
Ablation Study on VLMM-Reward Models. To evaluate the generality and accessibility our framework, replaced GPT-4V with open-source vision-language models (Qwen-VL 72B and 32B) as the reward model. Results show that RePrompt still achieves competitive performance: 0.74 with Qwen-VL VL 72B and 0.73 with Qwen-VL 32B (vs. 0.76 with GPT-V),, confirming the method's robustness without reliance on proprictary tools. We will include full results and adoption with accessible reward models in the final version.
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Table 7: Ablation study of rewards on the GenEval benchmark.
Method
a
Single object
Two object
Counting
Colors
Position
Attribute binding
Overall t
FLUX
0.99
0.79
0.75
0.78
0.18
0.45
0,66
+Qwen2.5 3B
0.99
0.84
0.63
0.81
0.35
0.48
0.68
RePrompt
1.0
0.0
0.99
0.86
0.66
0.89
0.52
0.50
0.74
0.7
0.3
0.98
0.86
0.70
0.88
0.56
0.52
0.75
0.5
0.5
0.98
0.87
0.77
0.85
0.62
0.49
0.76
0.3
0.7
0.99
0.85
0.74
0.84
0.58
0.50
0.75
0.0
1.0
0.99
0.85
0.72
0.83
0.53
0.47
0.74
Table 8: Performance comparison of RePrompt with different VLM L.M-Reward models
Method
Single object
Two object
Counting
Colors
Position
Attribute binding
Overall
GPT-4V
0.98
0.87
0.77
0.85
0.62
0.49
0.76
Qwen2.5-VL. 72B
0.98
0.86
0.75
0.84
0,58
0.46
0.74
Qwen2.5-VL 32B
0.98
0.85
0.74
0.83
0.55
0.45
0.73
Training Dynamics with Different Visual Reasoning Rewards. We analyze the impact dif- ferent visual reasoning rewards on training dynamics by plotting training curves under various re- ward configurations.
shown in Figure training with single reward model leads unstable reward fluctuations and degraded sample quality. This instability arises from the limited supervi- sion capacity of single reward model, which may overfit to specific patterns or neglect important compositional aspects. In contrast, proposed multi-reward formulation baauuaatioonbalaniinng diverse rea- soning signals, enables smoother optimization and more consistent improvements across iterations. These results emphasize the importance of combining complementary reward models to achieve both stability and generalization.
Reward t2)
Reward t2i
0.85
0.80
MW N
0.50
0.45
0.75
0.40
0.70
0.35
0.65
0.90
w/ Image-Reward Reward
0.60
VLM Lew-Rd
0.25
20
60
100
Global Step
Global Step
(a)
(D)
Figure 7: Training curve with different visual reasoning rewards. Using single reward model leads to instability and suboptimal performance during training. In contrast, our balanced reward design-combing multiple specialized reward signals-yiee more stable convergence and higher final reward values.
Impact of Reasoning Length. We investigated the impact the reasoning length (maximum token limit for the CoTT on generation quality by comparing limits of 512, 1024, and 2048 tokens. As shown in Table the 512-token setting yields the optimal performance with an Overall Score of 0.76. We observe slight performance degradation as the length increases to 2048 (Overall: 0.73). Qualitative analysis suggests that excessively long reasoning chains may introduce hallucinations or dilute the core prompt instructions, making it harder for the T2I model to maintain focus. However, is worth noting that even at 2048 tokens, our method still significantly outperforms the FLUX baseline (0.66), demonstrating the robustness of approach across different context lengths.
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Table 9: Ablation study reasoning length (token limit). The 512-token setting achieves the best balance, while longer contexts (2048) show a slight decline but still outperform the baseline.
Method
Single object
Two object
Counting
Colors
Position
Attribute binding
Overall f
FLUX
0.99
0.79
0.75
0.78
0.18
0.45
0.66
Ours (512)
0.98
0.87
0.77
0.85
0.62
0.49
0.76
Ours (1024)
0.98
0,85
0.76
0,83
0.56
0,48
0.74
Ours (2048)
0.97
0.83
0.75
0.81
0.52
0.47
0.73
Table 10: Human evaluation reasoning traces (0-10 scale). High scores in Correctness and Con- sistency confirm that our method generates logically sound plans rather than just keywords. The slightly lower Conciseness reflects the necessary elaboration for T2I alignment.
Method
Correctness
Consistency
Conciseness
Safety
GPT-5
9.9
9.7
7.9
9.9
Ours
9.7
9.5
8.3
9.8
Evaluation of Reasoning Quality. To address the concern regarding the quality and role of the reasoning traces, we conducted a human evaluation on the generated Chain-of-Thoupe (CoT) texts across four dimensions (0-10 scale). As shown in Table 10 our method achieves high scores in Correctness (9.7) and Consistency (9.5), comparable to the GPT-5. This demonstrates that the model generates logically sound and hallucination-free reasoning. confirming that the improvement is not merely due keyword stuffing. While the RL process introduces model-preferred vocabu- lary-resulting in slightly lower Conciseness (8.3) due to necessary elaboration this mechanism is essential for reducing generation stochasticity and enhancing controllability. Thus, the effectiveness stems from the synergy of structured reasoning ensuring logical coverage and RL-driven phrasing optimizing the final alignment.
E GENERALIZATION STUDY
Generalization to Different Data Distributions. We conducted a comprehensive retraining of our model using completely new and diverse dataset, distinct from the GenEval benchmark dis- tribution. Specifically, instead of relying on GenEval-like templates, we employed Gemini 2.5 Pro simulate realistic user inputs derived from a large-scale collection of 100,000 diverse real-worid images. This procedure generated 100k supervised fine-tuning (SFT) samples and 10k reinforce- ment learning (RI.) samples, reflecting truly "in-the-wild" data distribution. We then evaluated the retrained model on the GenEval benchmark to measure its cross doos--distibbttin generalization. As shown in Table IT] our method yields significant performance gains compared to relevant baselines trained on the original datasets, demonstrating clear improvement in all reasoning aspects. For example, on the FLUX benchmark. our model trained on the new distribution achieves an Overall score of 0.75 versus 0.66 obtained by the FLUX baseline. This result strongly supports claim that the model has learned generalized reasoning and catructtoo-followiini capabilities rather than overfitting specific templates or benchmark patterns.
Generalization across VLM Backbones. To further validate the architectural generalization and scalability of our framework, we extended our training strategy to the Intern family (specifi- cally Intern VL3.55-BB WWng et al. (2025b)). As presented in Table 12 our method proves to be architecture-agnostic. yielding consistent improvements across all T2I baselines regardless of the underlying VLM backbone. Notably, the InternV--based model achieves superior performance compared to the Qwen2.5-3B version, boosting the Overall score on FLUX from 0.76 to 0.78, with significant gains in challenging tasks like Counting (0.77 - 0.81) and Position (0.62 - 0.65). This demonstrates that RePrompt is not only robust to model selection but also effectively leverages stronger backbone capabilities enhance instruction following.
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Table 11: Cross-Distribution Evaluation on GenEval. The model was trained on distinct "in- the-wild" dataset (generated by Gemini 2.5 Pro) to test generalization. Despite the distribution shift, RePrompt significantly improves performance across all backbones, particularly spatial positioning and attribute binding.
Method
Single object
Two object
Counting
Colors
Position
Attribute binding
Overall 1
FLUX (Base)
0.99
0.79
0.75
0.78
0.18
0.45
0.66
+ Ours (Wild)
0.98
0.86
0.75
0.85
0.58
0.48
0.75
SD3 (Base)
1.00
0.85
0.62
0.88
0.22
0.58
0.69
+ Ours (Wild)
0.99
0.85
0.59
0.85
0.57
0.59
0.75
PixArt-E (Base)
0.99
0.60
0.47
0.81
0.10
0.26
0.54
+ Ours (Wild)
0.98
0.65
0.58
0.82
0.43
0.37
0.63
Table 12: Generalization across VLM Backbones. We compare our method trained with Qwen2.5- 3B versus VL3.5-4.... The results show that our approach is architecture-agnosti and scales positively with stronger VLM backbones.
Method
Single object
Two object
Counting
Colors
Position
Auribute binding
Overall t
FLUX (Base)
0.99
0.79
0.75
0.78
0.18
0.45
0.66
Ours (Qwen2.5-3B)
0.98
0.87
0.77
0.85
0.62
0.49
0.76
+ Ours (Intern VL3
0.99
0.89
0.81
0.86
0.65
0.52
0.78
SD3 (Base)
1.00
0.85
0.62
0.88
0.22
0.58
0.69
+ Ours (Qwen2.5-3B)
0.99
0.86
0.60
0.86
0.59
0.60
0.75
+ Ours (Intern VL3.544B)
1.00
0.87
0.62
0.87
0.61
0.62
0.76
PixArt-E (Basc)
0.99
0.60
0.47
0.81
0.10
0.26
0.54
Ours (Qwen2.5-3B)
0.98
0.64
0.56
0.81
0.40
0.35
0.62
Ours (Intern L3.5-4B)
0.99
0.66
0.58
0.82
0.42
0.37
0.64
Generalization on Diverse and Long-form Narratives. To demonstrate RePrompt's capability on diverse and long-form narrative prompts, we evaluated its generalizability on the TilF-bench Weil et al. 2025). comprehensive benchmark that categorizes prompts by length (Short vs. Long) and complexity (Basic vs. Advanced). As shown in Table [3] our method demonstrates robust gener- alizability across multiple backbones. Crucially, addressing the concern about long-form narrative and complex scenarios, our method achieves significant improvements in the Long and Advanced categories. For instance, with FLUX, observe substantial gain of nearly 8 points on Advanced- Long prompts. This result suggests that our reasoning augmented framework effectively manages the higher cognitive load required for long-context descriptions, ensuring that complex narrative elements are structurally planned and preserved rather than being lost during generation.
crasning-auumeeter
Generalization across Architectures. To assess the versatility of our method beyond diffusion- based frameworks, conducted experiments BAGEL Deng et al. (2025) (a unified multimodal model) and Infinity Han et al./ (2024) (an autoregressive model). As detailed in Table 14 our method yields substantial improvements across both architectures. Specifically, for BAGEL, the Overall score increases from 0.79 to 0.86, with the most notable gain in Position (+0.15), indicating sig- nificantly better spatial control. For Infinity, Position (+0.16) driving the Overall score from 0.69 to 0.77. These results confirm that our RL- driven prompt optimization effectively adapts the distinct decoding mechanisms of autoregressive and unified models. By tailoring the reasoning strategy to the specific latent characteristics of each backbone, our method successfully mitigates their inherent weaknesses spatial and numerical reasoning, justifying the use of model-specific policy alignment.
observe marked boost in Counting (+0.13) and
Human Evaluation. To validate the practical utility and interpretability of our method beyond automatic metries, conducted human user study with 15 evaluators 30 randomly selected
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Table 13: Performance comparison TIlF-bench. Our method consistently enhances generation quality, particularly for long and advanced prompis.
Overall
Basic
Advanced
Model
Short
Long
Short
Long
Short
Long
FLUX
71.09
71.78
83.12
78.65
65.79
68.54
+ Ours
76.50
77.80
88.50
84.20
72.80
76.50
SD
67.46
66.09
78.32
77.75
61.46
59.56
+ Ours
72.10
71.50
83.50
82.00
67.50
66.80
PixArt- 62.00
58, 12
70.66
75.25
57.65
49.50
+ Ours
67.80
65.50
76.50
80.10
64.20
58.00
Table 14: Generalization on architecturally diverse models (Unified Multimodal and Autoregres- sive). Our method significantly improves performance on both BAGEL and Infinity, demonstrating effectiveness beyond diffusion- architectures.
Method
Single object
Two object
Counting
Colors
Position
Attribute binding
Overall
BAGEL
0.98
0.90
0.79
0.85
0.62
0.60
0.79
+ Ours
0.98
0.94
0.82
0.92
0.75
0.75
0.86
Infinity
0.97
0.85
0.45
0.85
0.54
0.48
0.69
+ Ours
0.99
0.91
0.58
0.90
0.70
0.54
0.77
examples. Evaluators rated samples on a scale of 1-5 across three dimensions: Text- Align- ment, Image Quality, and Prompt Readability. As presented Table [5] our method consistently outperforms both the original baselines and the Qwen2.5-enhame we achieve Text-Image Alignment score of 4.48 (vs. 3.72 for the baseline). Crucially, addressing the concern regarding the nature of the generated text, method maintains high Prompt Readabil- ity scores. This confirms that the RL optimization guides the model to produce coherent, human- understandable reasoning and prompts, rather than degrading into unreadable adversarial tokens.
versions. Notably, for FLUX,
F
MORE CASES
More Qualitative Comparison. Figure 10 12 and 13 present qualitative comparisons dif- ferent types of compositional prompts, including Position, Two-object, Color, and Attribute Binding. Compared to baseline methods such as DELL-E3, approach produces images that more faith- fully adhere to the spatial, numerical, and attribute constraints specified in the prompts. For instance, in Figure our method accurately grounds relative positions (e.g.. "above". "left of") by leveraging explicit reasoning, while DELL-E3 often fails to reflect such relations or misplaces objects entirely. Figure 8 highlights our advantage in handling prompts involving multiple objects, where baseline models tend to merge objects or hallucinate irrelevant content. Similarly, in Figure 9, our method is better at preserving specified colors for each object, whereas the baseline some- times misbinds colors or applies them inconsistently. Finally, as shown in Figure 10, our approach improves attribute binding, ensuring that each attribute is applied to the correct object without con- fusion. These results demonstrate the effectiveness of integrating reasoning to improve alignment between visual outputs and complex prompt semantics.
Qualitative Results on Open-Wort World Prompts. To verify the generalizability RePrompt be- yond standard object-centric benchmarks, we present qualitative results on open-world prompts in Figure g These prompts involve abstract concepts, specific artistic styles, and imaginative narra- tives, which pose significant challenges for base models due to their lack of concrete visual map- pings. As observed, RePrompt effectively bridges the gap between abstract user intent and concrete visual attributes. By leveraging Chain-of-Thougee reasoning, method decomposes high-level
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Table 15: Human evaluation results (1-5 scale). Our method significantly improves alignment and
quality while maintaining high prompt readability, confirming that the generated reasoning remains coherent and human-understandable.
Method
Text- lmat-lmage Alignment
Image Quality
Prompt Readability
FLUX
3.72
4.45
3.50
FLUX Qwen2.5 3B
3.95
4.48
4.25
FLUX Ours
4.48
4.55
4.38
SD 3
3.85
4.38
3.60
SD 3 + Qwen2.5 3B
3.88
4.40
4.22
SD 3 + Ours
4.32
4.46
4.35
PixArt-E
3.15
3.65
3,45
PixArt-E + Qwen2.5 3B
3.48
3.78
4.15
PixArt-E + Ours
3,85
3.92
4.28
stylistic and narrative descriptions into precise visual cues. Consequently, the generated images not only adhere to the complex constraints but also maintain high aesthetic quality, demonstrating that RePrompt is robust for diverse, real-world creative applications.
User prompt
RePrompt
humonous and demiled photo of cat werring glasses and a small lis, siting offine dok The cat perched or office chair, socused intendly
sereen, with keyboud cup nearby. bockground features on organised office sotting with a window showing a cayscape. Soit. wamn lighting highlights the samre adding a oney feel againi the netral oflice colun.
photo working in in office.
with
insicate deialed lmngee sammnii ammor made miirlly bf white porceliin
displasing inditionll sumuni design with clasonne pattens reminiscem of Chinose porceiaits. The smoo's glasy and smooth light safly, emphasiring fngility. Small
sammri made poreclain, lngil,, glowey testare.
subdy accentuate deliesie appessance. sgainst minimalial vadisonal Japanese intrror with utmii mass, the amner stons on waedon disphy. illuminted by pentle spocighting that highlight
white colus.
realstei upose-up of sintage
watch. However, inside are not made of metal, but use sculpeed fiom tranilcent, wobdling fruit jelly vibrant
gears
casing dispays emate vintage engravings. Inside, the goin are desgoed from inmolsccut fuii jelly, wobbling slightly. with visunt ncon colors like elecnic bluc. line
hyper realistic up image vintge meclunical pocket watch.
The hands
warch hand dropp wax. is seep bubble, reflesting distoned, indessen view of room.
hot pink. rosembliing
glos enver,
thin film of wap bubble reflecting disunted roon.
Figure 8: Qualitative Results on Open- World Prompts. We showcase RePrompt's performance on diverse, imaginative, and stylized prompts. The results demonstrate that method effectively handles complex narratives and abstract concepts beyond simple object composition.
Failure Case Analysis. While RePrompt significantly enhances prompt adherence, we observe two primary failure modes through qualitative analysis as shown in Figure 9 The first hallucina- tion via over-reasoning, where the model generates an excessively verbose reasoning trace. This can lead to over-specification introducing specific constraints not present in the original user input. The second stems from intrinsic base model limitations. In scenarios involving extreme spatial overlaps, RePrompt may generate a precise and correct prompt, yet the underlying T2I model fails to render the visual attributes correctly due its own capacity constraints.
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User prompt
RePrompt
shimmeriing muden has crigitatically ahove sppported suppoets desolhe
close, smmctures, ais, larving the
A photo of bus ahove boot.
magically exsperded The water beneath the yacht mirrr like. perfeclly reflecting this buzaree plenomenos and amplllying iss suncal cffier. The the
bus shinmers occasoorally ly huuring sending rpples through the air like a minage. Pasengers on the yacht. unfured continee their icourcly axtiviiis, the flooring bis is an ondinary sight.
Imagine exquiett sceee festoring virrage compess with mirrcctt, aged brass detailing, stonng on a rualic wnoden ablle. viggesing in cra of explersion and discovery Above, a delicate glass prism hangs, capturing light and custing beautiful saidoow panerte
composs
the eleganely.
it, delicate gloss prisn langs, casing the left lis ancient key, while feather quill peints to right.
lef, ancient key amooie designs weatbered patina, hizting at my myseey and unold strcies. On the tight, feiller quill with will testures greefillly written advenures lighting gertle sludows anrching
ferwand himing The ambient.
riblzun
Figure 9: Visualization of Failure Cases. We observe two main failure patterns: (a) Wvar-Reas,nnn where the model hallucinates unrequested details due to excessive interpretation; and (b) Base Model Limitation, where the T2I model fails render complex attributes despite receiving a correct prompt.
G LIMITATIONS
While our method consistently improves compositional quality across standard T2I benchmarks, several limitations warrant future exploration. First, the performance gains certain fine-grained tasks-such as numeracy and object counting-reminn modest, suggesting potential for further en- hancement in precise quantitative reasoning. Second, our approach requires fine-tuning with compo- sitional supervision, which may introduce additional computational cost and reliance on structured training signals. However, this design is consistent with common RLHF setups and does not limit practical deployment. Third, the effectiveness of RePrompt depends on the quality of the reward model; while we demonstrate robustness across standard evaluators, improvements in reward fi- delity could further amplify performance.
H LLM USAGE
In this work, large language models (LLMs) were utilized primarily general- assistive tool to help with text polishing and improving writing clarity. The core research ideation, experi- mental design, analysis, and conclusions were developed solely by the authors. The LLM did not contribute to generating any original scientific content or ideas. The authors take full responsibility for the contents of the paper, including all text generated revised with the assistance of LLMss.
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User prompt
DELL-E3
Reasoning
Ours
Let's break down
The table could be footiing in mid-air, or it could be suspended somehow, The suitease should be the ground beneath the table
Prompt: "a photo dining table above a suitcase" "
Let's break it down close-up image of modern smartphone placed on the left side of well- used tennis racket. Both items are set against simple background...
Prompt: photo of a cell phone left of tennis racket"
Let's break it down
the cat in the foreground
and the backpack is in the
background. The backpack
could be hanging or placed
on something, but it's not
specified...
Prompt: "a photo of below backpack"
Let's break it down
we could add some
additional details such as the
type of bird, posture,
the setting in which
found. Let's assume the bird is a robin, perched on a branch near the couch... ...
Prompt: "a photo of'a bird of couch"
Figure 10: Qualitative results compositional prompts (Position). We compare DELL-E3, the intermediate reasoning process, and our final RePrompt outputs. While DELL-E3 often struggles with spatial relations (e.g.. object positions), our reasoning soruuiiingggidddddd guided approach enables more accurate and faithful generations that better match the prompt's compositional constraints.
25

---

Published as a conference paper at ICLR 2026
User prompt
DELL-E3
Reasoning
Ours
Let's break down The bear could be in playful or curious position, perhaps looking at the hair dryer curiously. The dryer could placed on a surface like a table countertop...
tr Reer
Prompt: "a photo of hair drier and a bear"
Let's break down
The toilct could be standard porcelain one. while the computer could
modern wireless placed side by side...
Prompt: "a photo of toilet and computer mouse"
Let's break it down
The positioning of these
objects isn't specified, so they could be placed side by side, above the other, orr some other aarrangement...
Prompt: "a photo of bottle and refrigerator"
Let's break it down
The toothbrush could be placed surface like bathroom countertop sink. while the snowboard could leaning against wall laid flat on a wooden floor.
Prompt: "a photo of toothbrush and snowboard"
Figure 11: Qualitative results compositional prompts (Two-objecc). We show comparisons among DELL-E3, our reasoning process, and RePrompt outputs on prompts involving two objects. DELL- E3 often fails to generate both entities accurately, whereas method uses explicit reasoning to guide the model in generating semantically correct and compositionally faithful images.
26

---

Published as a conference paper at ICLR 2026
User prompt
DELL-E3
Reasoning
Ours
Let's break down I'll ussume the hot dog is green, and the rest of the is normal...
Prompt: "a photo of green hot dog"
Let's break it down
This is unusual because zebrus typically black and white. I will assume zebra with usual black and white stripes but in vibrant shade of red...
Prompt: "a photo of red zebra"
Let's break down
This is somewhat unusual because oranges are typically orunge in color. However, it could be interpreted white-colored fruit fruit that appears white its texture or llighting conditions...
Prompt: photo of a white orange"
Let's break down
The color of the table is specified as blue, which should be the primary image. will assume a neutral background to keep the focus the table....
Prompt: "a photo of blue dining table"
Figure 12: Qualitative results on compositional prompts (Color). We present qualitative compar-
isons on prompts involving specific color attributes. While DELL-ES tends ignore or misinterpret color constraints, our approach leverages explicit reasoning to ensure accurate color grounding for each object, resulting in more faithful visual compositions.
27

---

Published as a conference paper at ICLR 2026
User prompt
DELL-E3
Reasoning
Ours
Let's break down I'll the hot dog green, and the of is normal.
Prumpt: photo of a yellow bicycle motorcycle" "
Let's break down vibrant pink TV remole and airplane. Both items svivid blue hould be placed side by side...
Ciub
Prompt: photo of pink remote and blue uirplane"
Let's break it down
The tie should be blue,
which suggests it could be
vibrant shade blue
perhaps subducd
The dining table should
pink, which indicates
pastel shade pink. ....
Prompt: "a photo of blue tie and pink dining table"
Let's break down
vibrant orange truck
parked next strikingly
pink sink.. ...
Prompt: "a photo of orange truck and pink sink"
Figure 13: Qualitative results on compositional prompts (Attribute binding). We show examples where prompts specify multiple attributes that must correctly associated with the correspond- ing objects. Our method utilizes step-by-step reasoning disambiguate attribute-object bindings, avoiding attribute swaps or omissions that are common in baseline outputs.
28


Reviews:
[{"review_id": "RDM9cuRzNf", "reviewer": "Reviewer_VzC3", "summary": "In this paper, the authors propose a new method for automated prompt engineering for text-to-image generation. In particular, they use the standard RL training method for LLM and train an LLM to perform this specific task. They compare their method with several baselines and show improvements.", "questions": "Have the authors trained from instruct models as opposed to base models? How much can this method improve the performance?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "1. The paper is clearly written and easy to follow.\n2. Table 2 shows great transferability of the prompts among different text-to-image models.\n3. The ablation study conducted in this paper is very thorough.", "weaknesses": "1. My main concern about this paper is regarding its novelty. The training procedure of their model is a very standard RL recipe for LLM, and using LLM as an automated prompt generator for text-to-image generation is not a new idea either (e.g. Hao et al. (2023); Mo et al. (2024); Yeh et al. (2024); Ma ̃nas et al. (2024); Yun et al. (2025); Cao et al. (2023); Qin et al. (2024); Yang et al. (2024d); Wu et al. (2024); Wang et al. (2024) in the paper). It seems like this paper would be better suited for other venues like TMLR.\n2. The authors claim that prior LLM prompt generation methods “frequently generate prompts that produce images with semantically inconsistent or visually implausible content, such as conflicting object placements or unrealistic interactions, because the underlying LLMs lack grounding in physical reality and do not incorporate feedback from downstream visual task” “ with limited generalization”. However, neither of the claims are supported by their experiments. Specifically on generalizability, the model that the authors propose is trained on a dataset curated by following the prompt construction in the GenEval benchmark and evaluated on the same benchmark plus another small benchmark with only 300 test examples. It is unclear to me why these experiments can warrant claims w.r.t. better generalizability.\n3. The authors use GPT-4V, a deprecated model in the GPT family for comparison, not only that it is impossible to replicate the result, it also renders the comparison a bit outdated. It would be better if the authors can compare their method with newer GPT models (would be even better to include comparison with a standard model and a reasoning model).\n4. The authors have also only finetuned from Qwen models and it would be nice to show results from other model families.\n5. The authors can consider adding discussions and/or comparisons to the following papers:\n\nYeh et al. TIPO: Text to Image with Text Presampling for Prompt Optimization. 2024.\n\nLu et al. Language models as black-box optimizers for vision-language models. 2024.\n\nHe et al. Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation. 2024.", "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": "wkJRCPI27F", "reviewer": "Reviewer_mYzy", "summary": "This paper proposes RePrompt, a novel framework that uses Reinforcement Learning (RL) to train a Large Language Model (LLM) to enhance text-to-image (T2I) prompts. It indicates that the generation of a structured, self-reflective \"reasoning trace\" alongside the enhanced prompt helps ground the prompt in visual semantics and improve compositional accuracy. The method is trained end-to-end using a tailored ensemble reward model that assesses image quality, semantic alignment, and prompt structure. Experiments on GenEval and T2I-Compbench show improvements, especially in spatial understanding, over strong LLM-enhanced baselines across multiple T2I backbones.", "questions": "Please see weaknesses", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The integration of explicit, structured reasoning with RL for prompt enhancement is a well-motivated approach. It effectively bridges the gap between linguistic fluency and visual plausibility that plagues LLM-based prompters.\n2. The paper demonstrates consistent performance gains across three different diffusion-based T2I models (FLUX, SD3, Pixart-Σ). The improvements in challenging areas like spatial reasoning are compelling.\n3. The framework is designed to be T2I model-agnostic, requiring no retraining of the image generator. The reported inference latency (30s) is significantly lower than iterative optimization baselines, making it more practical.", "weaknesses": "1. The training and evaluation prompts are heavily focused on object-centric, compositional generation (training prompts sourced from GenEval-like templates). This raises a concern about potential overfitting to the specific categories and styles of the benchmarks used.\n\n2.  It is unclear how RePrompt would perform on more diverse, stylized, imaginative, or long-form narrative prompts that are common in real-world use. \n\n3. While the paper shows generalization across diffusion-based models, its performance on architecturally different T2I models (e.g., autoregressive or unified multimodal models) remains unverified. Furthermore, the claim of being \"model-agnostic\" is slightly tempered by the finding that the policy is \"individualized to each T2I backbone.\" This suggests that to achieve optimal performance on a new T2I model, one might need to retrain the RePrompt LLM via RL, which is computationally expensive and reduces plug-and-play utility.\n\n4. The fundamental problem might be mitigated by future, more capable T2I models that inherently possess better compositional reasoning. If such models emerge, the value of an add-on module that requires significant RL training could diminish. \n\n5. The instructions provided to the LLM-enhancer baselines (Qwen2.5, GPT-4, etc.) are not included. The performance of these baselines is highly sensitive to how they are prompted, providing this information would increase the transparency and reproducibility of this work.\n\n6. What are the common failure cases for RePrompt? For instance, does the reasoning trace ever lead to over-specification or introduce its own hallucinations? Examples of such failures would help users understand the limitations of the current approach.", "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": "8oJPXTTQ9D", "reviewer": "Reviewer_k9Mw", "summary": "This paper proposes reasoning-augmented framework for text-to-image generation. Unlike previous methods that rely on rewriting or heuristic feedback loops, RePrompt trains an auxiliary LLM via reinforcement learning (RL) that generates both reasoning traces and refined prompt to further prompt a frozen text-to-image model. The proposed method has been extensively evaluated on GenEval and T2I-Compbench datasets, achieving consistent improvements across backbones (FLUX, SD3, PixArt).", "questions": "- Is human-in-the-loop verification needed to ensure that the reward aligns well the perceptual alignment?\n\n- What is the impact of reasoning length on the generation quality?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The idea of combing LLM reasoning and image-level feedback is novel and promising. \n\n- The reward is also well designed. First of all, the visual-reasoning reward acts as a bridge to connect image reward (human preference alignment) with semantic grounding (VLM reward). Second, it allows the reward to depend only on the behavior of input and output, enabling model-agnostic characteristic of RePrompt across different T2I backbones.\n\n- The ablations and theoretical analysis (in Appendix B), together with the empirical results, all justify the design of RL in this paper, especially on GRPO optimization, and reasoning traces which acts as variance-reduction condition. \n\n- The paper is well presented and the reproducibility is good.", "weaknesses": "- The evaluation benchmarks are only object-centric datasets. The performance on open-world prompt is not verified. It is better to show several examples on this scenario.\n\n- No failure cases in the visualization. What is the model behavior on rare, free-form prompts not covered in GenEval? For example, \"a photo of a cat working in an office\"", "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": "LtOGkMZipM", "reviewer": "Reviewer_bm45", "summary": "This paper proposes RePrompt, a prompt enhancement framework for text-to-image (T2I) generation models that introduces explicit reasoning into the prompt generation process and optimizes based on image-level outcomes using reinforcement learning. The reward model adopts an ensemble reward that evaluates three dimensions: human preference, visual realism, and semantic alignment. On the GenEval benchmark, significant improvements in spatial layout fidelity and compositional generalization are observed, and on T2I-Compbench, particularly notable score improvements in spatial compositions are confirmed.", "questions": "- Do you plan to conduct human evaluation? Evaluating the quality of generated images and readability of prompts from the perspective of actual users would demonstrate practical utility that cannot be captured by automatic metrics alone.# Review", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The proposed method fixes the image generation model and optimizes only the language model policy, making it applicable to any existing T2I backbone. Since the reward model depends only on the prompt-image output pair and not on any specific T2I architecture, it generalizes naturally across different generation backbones and unseen prompt distributions.\n\n- Substantial improvements are observed in compositional understanding, particularly in spatial position (Position). In the Position category of GenEval, remarkable relative improvements are achieved compared to the Qwen2.5 3B baseline: FLUX (+77.1%), SD3 (+78.8%), and Pixart-Σ (+122.2%). Overall GenEval scores also consistently improve across each backbone (+11.8%, +10.3%, +6.9%).\n\n- In terms of inference latency, the method is significantly faster (30s per image) compared to Idea2Img (140s per image) and PARM++ (110s per image), while also achieving the highest accuracy (0.76), demonstrating practical advantages.\n\n- In qualitative evaluation, concrete examples are presented, showing that for prompts such as \"a fire hydrant with a tennis racket\" and \"a photo of a dog above a cow,\" the method avoids object fusion and misplacement observed in baseline models and faithfully reproduces the intended spatial composition.", "weaknesses": "- **Limited scope of evaluation**: The main evaluation relies solely on two automatic evaluation benchmarks: GenEval and T2I-Compbench. Human evaluation is not included, so the practical utility aspects such as \"whether actual users find the results convincing,\" \"whether generated prompts are readable,\" and \"whether reasoning explanations are appropriate\" depend solely on automatic metrics.\n\n- **Insufficient comparison with closely related methods**: Prior work on prompt enhancement mentions iterative refinement approaches and single-pass LLM-based enhancement methods, and quantitative comparisons with Promptist, PAG, GPT4, Deepseek-r1, and Qwen2.5 are provided in tables. However, detailed analysis from the perspective of RL-based prompt optimization is insufficient. Therefore, the theoretical and experimental justification for \"why CoT (Chain-of-Thought) with RL is superior to conventional prompt optimization\" is somewhat weak.\n\n- **Lack of CoT quality evaluation**: While the method generates reasoning traces that simulate visual implications of prompts—much like how humans mentally visualize a scene—and this structured, logic-driven process anticipates potential errors during prompt construction, direct evaluation of the reasoning text itself in terms of correctness, consistency, and conciseness is not performed. The claim that \"reasoning is effective\" is made indirectly through final image scores, but it is not clearly separated whether \"performance improved because of structuring\" or \"merely because detection-friendly phrases were added.\"\n\n- **Insufficient discussion of safety and robustness**: The design optimizes prompt generation through reinforcement learning, but there is no discussion of risks where RL might learn extreme descriptive expressions, overly detailed specifications, or expressions that deceive the reward model, nor mechanisms to detect or suppress such behaviors. Although the Broader Impact section mentions risks of generating misleading content and bias propagation, and recommends pairing the method with content moderation filters and fairness-aware training objectives, concrete countermeasures or experimental validation are not included.", "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)