RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning

## Abstract

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 is available at: https://github.com/microsoft/DKI_LLM/tree/main/RePrompt .

## 1 Introduction

Text-to-image (T2I) generation has made rapid progress with the rise of large-scale generative models flux2024; esser2024scaling; podell2023sdxl; chen2024pixart, yet a persistent challenge remains: users typically provide concise and under-specified prompts, which often result in images that fail to reflect the intended semantics or visually coherent compositions. Generated outputs 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 yang2024mastering.

Previous work on prompt enhancement in T2I can be divided into two main approaches. The first approach focuses on iterative refinement: an image is generated from an initial prompt, and subsequent feedback, derived either from human preference models or from automated scoring systems, is used to improve the prompt or intermediate representations over multiple rounds yang2024idea2img; wu2024self; wang2024genartist; guo2025can. Although this approach can progressively improve image quality, it suffers from high latency and computational overhead due to repeated image generation, and it 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 betker2023improving; hao2023optimizing. While these methods produce linguistically fluent and expressive descriptions, they 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 tasks. As a result, they frequently hallucinate content or miss critical spatial and attribute-level relationships (see Figure [1](https://arxiv.org/html/2505.17540v1#S1.F1)).

Figure: Figure 1: Given the user prompt "a photo of a couch below a vase", existing models like DELL-E3 generate rich language descriptions but often produce unrealistic or physically implausible compositions. In contrast, our RePrompt performs explicit chain-of-thought reasoning to resolve spatial relations, resulting in enhanced prompts that guide text-to-image models towards realistic and semantically aligned generations.
Refer to caption: x1.png

In contrast, we propose RePrompt, a reasoning-augmented prompt refinement framework trained via reinforcement learning. Rather than relying on stylistic rewriting or black-box completions, RePrompt trains a 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 guo2025deepseek; trung2024reft; team2025kimi; jaech2024openai, RePrompt enables the model to internally simulate the visual implications of a prompt—much like how humans mentally visualize a 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 a 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 reinforcement learning. To capture the multifaceted nature of image quality, we design a 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 a 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 ghosh2023geneval and T2I-Compbench huang2023t2i demonstrate that RePrompt significantly improves compositional accuracy, semantic fidelity, and spatial coherence. Notably, on the GenEval benchmark, RePrompt surpasses Qwen2.5 3B-enhanced baselines by +77.1% (FLUX flux2024), +78.8% (SD3 esser2024scaling) and +122.2% (Pixart-$\Sigma$ chen2024pixart) 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 an order-of-magnitude lower latency than optimization-heavy baselines like Idea2Img (30s vs. 140s per image), offering a scalable and inference-efficient solution. These findings validate the effectiveness of explicit reasoning in prompt construction for closing the semantic-visual alignment gap in text-to-image generation, without relying on larger language models or expensive optimization at inference time.

Figure: 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 a T2I model, which are then scored by a reward model. Feedback is used to update RePrompt via GRPO.
Refer to caption: x2.png

## 2 Related Work

#### Text to Image Generation.

Recently, large-scale diffusion models flux2024; esser2024scaling; podell2023sdxl; chen2024pixart; xie2025sana; liu2024playground; betker2023improving; ma2025inference; rombach2022high; saharia2022photorealistic 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 hao2023optimizing; mo2024dynamic; yeh2024tipo; manas2024improving; yun2025learning; cao2023beautifulprompt; qin2024diffusiongpt; yang2024idea2img; wu2024self; wang2024genartist, often relying on manual or heuristic strategies with limited generalization. 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 guo2025deepseek; wallace2024diffusion; yang2024dense; guo2024versat2i; gupta2025simple; lee2024parrot; zhao2024scores; nabati2024personalized; black2023training; liang2024rich; kirstain2023pick; lee2023aligning; shen2025vlm; zhang2024itercomp; wang2025simplear; jiang2025t2i; xue2025dancegrpo have been employed to fine-tune outputs by maximizing a reward function that encapsulates desired attributes such as image realism, diversity, and semantic fidelity. In our framework, we adopt RL 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, T2I-R1 jiang2025t2i is closely related to our work, but it targets Janus-Pro chen2025janus, a unified vision-language model. In contrast, our method trains an auxiliary LLM that generalizes across diverse text-to-image models, offering a more flexible and model-agnostic solution.

#### Reasoning in LLM.

Reasoning in LLMs wei2022chain improves complex task solving by decomposing problems into intermediate steps feng2025video; huang2025vision; yang2025r1; zhang2025r1; huang2025boosting; yu2025perception; ma2025deepperception; li2025videochat; lu2025ui. In multimodal generation, reasoning mechanisms yang2024mastering; guo2025can; wang2025mint; zhang2025layercraft; sahili2024faircot; chen2024training enhance prompt understanding and semantic alignment. Our method integrates reasoning with reinforcement learning, enabling step-wise prompt refinement that improves interpretability, text-image alignment, and image quality—offering a new perspective for prompt optimization in T2I generation.

## 3 Method

We present RePrompt, a reasoning-augmented reprompting framework for text-to-image (T2I) generation. RePrompt decouples prompt generation from image generation, i.e., training a language model to produce structured, semantically rich prompts, while keeping the T2I backbone fixed. We optimize RePrompt 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](https://arxiv.org/html/2505.17540v1#S1.F2)):
1) a Prompting Policy $\pi_{\theta}$, which produces a *reasoning trace* $H$ and an enhanced prompt $P^{\prime}$;
2) a fixed T2I Synthesizer $f_{\phi}$, which renders an image $I$ from $P^{\prime}$;
3) a T2I RePrompt Reward Model $R_{\mathrm{total}}(I,P,P^{\prime})$, which scores the image on realism, semantic alignment, and prompt structure.

Given an input prompt $P$, the policy samples:
$y=(H,P^{\prime})\sim\pi_{\theta}(P)$,
the synthesizer then generates:
$I=f_{\phi}(P^{\prime})$. Since $f_{\phi}$ is non-differentiable, we formulate prompt generation as a single-step Markov Decision Process (MDP):
State: the original prompt $P$. Action: sampling $y=(H,P^{\prime})\sim\pi_{\theta}(y\mid P)$. Transition: deterministic mapping $P^{\prime}\mapsto I=f_{\phi}(P^{\prime})$. Reward $r$: the reasoning reward $r=R_{\mathrm{total}}(I,P,P^{\prime})$. Objective: maximize $E_{P\sim\mathcal{D}}\big{[}E_{y\sim\pi_{\theta}(y\mid P)}[r]\big{]}$.

We train $\pi_{\theta}$ via reinforcement learning (Group Relative Policy Optimization, GRPO guo2025deepseek) to improve downstream image quality. By keeping the T2I model $f_{\phi}$ fixed, RePrompt learns backbone-specific reasoning strategies that enhance semantic fidelity and visual realism without requiring any manually annotated reasoning traces.

### 3.2 T2I RePrompt Reward Model

Figure: Figure 3: The Visual-Reasoning Reward.
Refer to caption: x3.png

A central component of our framework is the T2I RePrompt Reward Model—an ensemble, image-grounded reward function specifically designed for the prompt refinement task in T2I generation. In contrast to generic reward functions used in natural language or vision tasks, our reward model is co-developed with the objective of enhancing reasoning-driven prompt construction. It explicitly evaluates whether a generated prompt yields an image that is *realistic*, *semantically faithful to the user intent*, and *compositionally coherent*.

This reward framework is critical to training RePrompt effectively and serves three key goals:
(1) Stable optimization: Each component provides dense and structured feedback, mitigating the challenges 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 generalization: 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 text-to-image generation.

#### Visual-Reasoning Reward ( R vis subscript 𝑅 vis R_{\mathrm{vis}} italic_R start_POSTSUBSCRIPT roman_vis end_POSTSUBSCRIPT ).

This component (as shown in Figure [3](https://arxiv.org/html/2505.17540v1#S3.F3)) captures both user-aligned preferences and semantic correctness at the image level:

$$ $R_{\mathrm{vis}}=\alpha\,R_{\mathrm{pref}}^{\text{IMG}}+\gamma\,R_{\mathrm{sem }}^{\text{VLM}}.$ (1) $$

Here, $R_{\mathrm{pref}}^{\text{IMG}}$ is derived from ImageReward xu2023imagereward, which used to evaluate whether generated images align with human preferences. $R_{\mathrm{sem}}^{\text{VLM}}$ is obtained from VLM-Reward achiam2023gpt, which evaluates semantic consistency and visual quality using a vision-language model. The weights $\alpha$ and $\gamma$ allow us to control the trade-off between perceived image quality and factual alignment.

#### Structure Reward ( R struc subscript 𝑅 struc R_{\mathrm{struc}} italic_R start_POSTSUBSCRIPT roman_struc end_POSTSUBSCRIPT ).

To ensure that the generated output maintains a clear reasoning-to-prompt format, we enforce a structured syntax:

> <reason>…</reason><prompt>…</prompt>

We apply a binary reward:

$$ $R_{\mathrm{struc}}=\begin{cases}+1,&\text{if format is correct}\\ -1,&\text{otherwise}\end{cases}$ (2) $$

This reward encourages models to adhere to a standardized output layout, simplifying downstream parsing and ensuring the interpretability of reasoning traces.

#### Length Reward ( R len subscript 𝑅 len R_{\mathrm{len}} italic_R start_POSTSUBSCRIPT roman_len end_POSTSUBSCRIPT ).

To ensure compatibility with real-world T2I models such as SDXL and FLUX.1, which impose token-length limits, we apply a constraint on prompt length:

$$ $R_{\mathrm{len}}=\begin{cases}+1,&L_{\min}\leq L\leq L_{\max},\\ -1,&\text{otherwise}\end{cases}$ (3) $$

where $L$ is the token length of $P^{\prime}$, and $[L_{\min},L_{\max}]$ is empirically set to 15 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:

$$ $R_{\mathrm{total}}=R_{\mathrm{vis}}+R_{\mathrm{struc}}+R_{\mathrm{len}}.$ (4) $$

The RePrompt policy $\pi_{\theta}$ is trained to maximize expected downstream reward:

$$ $\theta^{\ast}=\arg\max_{\theta}\;\mathbb{E}_{P\sim\mathcal{D},\,y\sim\pi_{ \theta}(y\mid P)}\left[R_{\mathrm{total}}(I,P,P^{\prime})\right],$ (5) $$

where $I=f_{\phi}(P^{\prime})$ and $f_{\phi}$ is a 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, our 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 $f_{\phi}$. RL allows us to optimize $\pi_{\theta}$ directly with respect to *downstream image outcomes* rather than proxy losses on text. Moreover, because RePrompt is *individualized to each T2I backbone*, it can adapt its reasoning and prompt style to the specific strengths and limitations of a given model—improving generalization and image fidelity without retraining $f_{\phi}$.

At each update step, for a given user prompt $P$, we sample a set of $G$ candidate outputs $\{y_{i}\}_{i=1}^{G}\sim\pi_{\theta_{\mathrm{old}}}(y\mid P)$. Each candidate $y_{i}=(H_{i},P^{\prime}_{i})$ yields an image $I_{i}=f_{\phi}(P^{\prime}_{i})$ and receives a scalar reward $r_{i}=R_{\mathrm{total}}(I_{i},P,P^{\prime})$. We then compute normalized advantages:

$$ $A_{i}=\frac{r_{i}-\mu_{r}}{\sigma_{r}},\quad\mu_{r}=\frac{1}{G}\sum_{j=1}^{G}r _{j},\quad\sigma_{r}=\sqrt{\frac{1}{G}\sum_{j=1}^{G}(r_{j}-\mu_{r})^{2}}.$ (6) $$

We use Group Relative Policy Optimization (GRPO) guo2025deepseek as a practical and stable update method for training the reprompting policy based on group-wise reward comparisons. The objective is defined as:

$$ $\displaystyle\mathcal{J}_{\mathrm{GRPO}}(\theta)=E_{P,\{y_{i}\}\sim\pi_{\theta _{\mathrm{old}}}}\Bigg{[}\,\frac{1}{G}\sum_{i=1}^{G}$ $\displaystyle\min\!\Big{(}\rho_{i}\,A_{i},\;\mathrm{clip}(\rho_{i},1- \varepsilon,1+\varepsilon)\,A_{i}\Big{)}$ $\displaystyle-\beta_{\mathrm{KL}}\;KL\big{(}\pi_{\theta}(y\mid P)\,\|\,\pi_{ \mathrm{ref}}(y\mid P)\big{)}\Bigg{]},$ (7) $$

where
$\rho_{i}=\frac{\pi_{\theta}(y_{i}\mid P)}{\pi_{\theta_{\mathrm{old}}}(y_{i}
\mid P)}$,
$\varepsilon$ and $\beta_{\mathrm{KL}}$ are clipping and penalty coefficients,
$\pi_{\mathrm{ref}}$ is a reference policy (e.g., the initial or distillation policy). By fixing $f_{\phi}$ and optimizing only $\pi_{\theta}$, RePrompt can be applied *universally* to any off-the-shelf T2I model, learning backbone, specific 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 [A](https://arxiv.org/html/2505.17540v1#A1).

## 4 Experiments

### 4.1 Settings

#### Implementation Details.

We use Qwen2.5-3B yang2024qwen2 as our base language model. For the text-to-image model used for training, we use the FLUX.1-dev flux2024 model, which generates images at a resolution of 512×512 pixels.
Our model is trained using the TRL (^1^11https://github.com/huggingface/trl) reinforcement learning framework for 3 epochs, with 4 outputs generated per instance, the weight of ImageReward xu2023imagereward and VLM-Reward are both 0.5. The VLM used for computing VLM-Reward is GPT-4V. All experiments were conducted on 8 NVIDIA A100 (80GB) GPUs, and the entire training process required about 6 hours. More details are in the Appendix [B](https://arxiv.org/html/2505.17540v1#A2).

#### Training Data.

Inspired by the prompt construction strategy in GenEval ghosh2023geneval, we adapt six object-centric templates to a newly curated list of 288 common daily objects generated via GPT-4 achiam2023gpt. 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 the impact of RePrompt, we evaluate on two benchmarks: GenEval ghosh2023geneval and T2I-Compbench huang2023t2i. GenEval focuses on instance-level alignment with user intent using concise prompts, while T2I-Compbench measures compositional generation under complex scenarios involving multiple objects, attributes, and spatial relations.

**Table 1: Evaluation of text-to-image generation on the GenEval benchmark. Our method consistently outperforms strong baselines, achieving the best overall scores. Notably, our approach shows substantial gains in spatial position understanding over Qwen2.5 3B-enhanced baselines, demonstrating its superior capability in grounding spatial relations.**
| Method | Single | Two | Counting | Colors | Position | Attribute | Overall $\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | object | object |  |  |  | binding |  |
| FLUX flux2024 | 0.99 | 0.79 | 0.75 | 0.78 | 0.18 | 0.45 | 0.66 |
| +GPT4 | 0.99 | 0.79 | 0.68 | 0.84 | 0.51 | 0.52 | 0.72 |
| +Deepseek-r1 | 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 w/ FLUX) | 0.98 | 0.87 | 0.77 | 0.85 | 0.62 | 0.49 | 0.76 |
| \hdashlineImprovement over Qwen2.5 3B | -1.0% | +3.6% | +22.2% | +4.9% | +77.1% | +2.1% | +11.8% |
| SD3 esser2024scaling | 1.00 | 0.85 | 0.62 | 0.88 | 0.22 | 0.58 | 0.69 |
| +GPT4 | 1.00 | 0.84 | 0.51 | 0.85 | 0.48 | 0.54 | 0.70 |
| +Deepseek-r1 | 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 |
| \hdashlineImprovement over Qwen2.5 3B | -1.0% | 0.0% | +13.2% | +2.4% | +78.8% | +9.1% | +10.3% |
| Pixart-$\Sigma$ chen2024pixart | 0.99 | 0.60 | 0.47 | 0.81 | 0.10 | 0.26 | 0.54 |
| +GPT4 | 0.96 | 0.67 | 0.48 | 0.84 | 0.36 | 0.31 | 0.60 |
| +Deepseek-r1 | 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 3B | 0.99 | 0.68 | 0.48 | 0.82 | 0.18 | 0.32 | 0.58 |
| +Ours (train w/ FLUX) | 0.98 | 0.64 | 0.56 | 0.81 | 0.40 | 0.35 | 0.62 |
| \hdashlineImprovement over Qwen2.5 3B | -1.0% | -5.9% | +16.7% | -1.2% | +122.2% | +9.4% | +6.9% |

**Table 2: Evaluation of text-to-image generation on the T2I-Compbench. 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 | 0.7149 | 0.5103 | 0.6012 | 0.2579 | 0.5982 | 0.3325 |
| +Ours (train w/ FLUX) | 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-$\Sigma$ | 0.5682 | 0.4717 | 0.5622 | 0.2497 | 0.5366 | 0.3655 |
| +Qwen2.5 3B | 0.6618 | 0.4814 | 0.5662 | 0.2481 | 0.5443 | 0.3335 |
| +Ours (train w/ FLUX) | 0.6665 | 0.5011 | 0.6190 | 0.2913 | 0.5716 | 0.3680 |

### 4.2 Comparision

Table [1](https://arxiv.org/html/2505.17540v1#S4.T1) presents the performance comparison across various text-to-image generation models evaluated on the GenEval benchmark, which assesses six fine-grained 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 flux2024 , our approach achieves a 0.62 score on Position, representing a +77.1% relative improvement over the Qwen2.5 3B baseline. Similarly, for SD3 esser2024scaling, we observe a +78.8% gain, and for Pixart-$\Sigma$ chen2024pixart, the relative improvement reaches an impressive +122.2%. This strong enhancement highlights the strength of our compositional training strategy in explicitly grounding spatial relations between objects. Beyond the Position metric, our method also achieves substantial improvements in Counting (+22.2% for FLUX, +13.2% for SD3, +16.7% for Pixart-$\Sigma$) 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-$\Sigma$). These results verify the effectiveness of our 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-the-art advances in spatial understanding.

**Table 3: Ablation study of SFT and RL on the GenEval benchmark.**
| Method | Single | Two | Counting | Colors | Position | Attribute | Overall $\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | object | object |  |  |  | binding |  |
| FLUX flux2024 | 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 |
| w/ SFT + RL | 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 accuracy and latency with the subset of Geneval. All latency is measured on a single NVIDIA A100 GPU.**
| Method | Accuracy ↑ | Latency (s / img) ↓ |
| --- | --- | --- |
| FLUX | 0.65 | 20 |
| Show-o xie2024show | 0.55 | 3 |
| Idea2Img yang2024idea2img (w/ FLUX) | 0.69 | 140 |
| PARM++ guo2025can (w/ Show-o) | 0.72 | 110 |
| RePrompt (w/ FLUX) | 0.76 | 30 |

### 4.3 Generalization Performance on T2I-Compbench

We further assess the robustness of our method on the T2I-Compbench benchmark, which tests compositional generalization across six dimensions: color, shape, texture, spatial reasoning, numeracy, and complex attribute combinations. As shown in Table [2](https://arxiv.org/html/2505.17540v1#S4.T2), RePrompt consistently improves performance across all evaluated backbones. In particular, it significantly boosts spatial compositional scores (e.g., from 0.2494 to 0.3301 on 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 LLM-enhanced baselines, for instance, on Pixart-$\Sigma$, 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 [3](https://arxiv.org/html/2505.17540v1#S4.T3) shows the effect of supervised fine-tuning (SFT) and reinforcement learning (RL) on GenEval. SFT brings modest gains (+0.01 overall), mainly improving positional understanding (0.35→0.43), suggesting it helps inject object-attribute knowledge but struggles 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 improvements in spatial reasoning (0.62) and counting (0.77). These results confirm that SFT offers useful priors, while RL is key for compositional robustness.

Figure: 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.
Refer to caption: extracted/6470159/Figure/grouped_barplot.png

Figure: Figure 5: Qualitative results on compositional prompts. Compared to vanilla T2I models, our RePrompt improves spatial layout and object relations by generating enhanced prompts with explicit reasoning, leading to more faithful compositions.
Refer to caption: x4.png

#### Comparison on Accuracy and Latency.

Table [4](https://arxiv.org/html/2505.17540v1#S4.T4) 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 (0.65) compared to Show-o (0.55) but suffers from a longer latency (20s vs. 3s per image), reflecting a trade-off between quality and speed. When incorporating advanced prompting techniques, Idea2Img (w/ FLUX.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 110s latency.
In contrast, our RePrompt achieves the best accuracy (0.76) while maintaining a much lower latency (30s). This demonstrates the effectiveness of our design in enabling precise visual grounding without compromising efficiency, making it more practical for real-world deployment.

#### Ablation Study on Trained T2I Models.

Figure [4](https://arxiv.org/html/2505.17540v1#S4.F4) illustrates the generalization ability of RePrompt across three diverse T2I backbones: FLUX, SD3, and PixArt-$\Sigma$. Our method consistently boosts compositional scores on GenEval—improving FLUX from 0.66 to 0.76, SD3 from 0.69 to 0.75, and PixArt-$\Sigma$ from 0.54 to 0.62—outperforming both baselines and the +Qwen2.5 variants. These results demonstrate RePrompt’s robustness and model-agnostic design, working effectively across both large (e.g., SD3) and lightweight (e.g., PixArt-$\Sigma$) backbones. Notably, we observe that stronger T2I models used during training lead to better generalization at inference, likely due to richer reasoning patterns induced during learning.

#### Qualitative Comparison.

Figure [5](https://arxiv.org/html/2505.17540v1#S4.F5) illustrates the qualitative advantage of RePrompt over existing T2I models. Baseline models often generate images with incorrect spatial relations or hallucinated objects. For example, when prompted with “a fire hydrant with a tennis racket,” DALL-E 3 produces unrealistic, stylistic blends where the objects are merged. In contrast, RePrompt accurately grounds the tennis racket "around the base" of the fire hydrant, respecting the intended composition. Similarly, for “a photo of a dog above a 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 highlight RePrompt’s ability to mitigate typical failures in spatial arrangement and object interaction by generating prompts with explicit compositional cues. More cases are in the Appendix [D](https://arxiv.org/html/2505.17540v1#A4).

#### More Ablation Study.

We present additional ablation studies on reasoning, training dynamics, and reward functions in the Appendix [C](https://arxiv.org/html/2505.17540v1#A3) to further validate the effectiveness of our reasoning design and reward selection, as well as the stability of the training process.

## 5 Limitations

While our method consistently improves compositional quality across standard T2I benchmarks, several limitations warrant future exploration. First, the performance gains on certain fine-grained tasks—such as numeracy and object counting—remain modest, suggesting potential for further enhancement in precise quantitative reasoning. Second, our approach requires fine-tuning with compositional 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 fidelity could further amplify performance.

## 6 Conclusion

In this work, we propose a simple yet effective method to enhance compositional understanding in text-to-image (T2I) generation models. By injecting chain-of-thought (CoT) reasoning into the prompt construction pipeline and pairing each CoT with an enhanced prompt, our approach improves 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-$\Sigma$, without requiring additional model retraining. These results highlight the generalizability and plug-and-play nature of our method. We believe our approach offers a new perspective for improving controllability and compositional fidelity in generative models.

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## Appendix A Variance Reduction via Structured Reasoning

In this appendix, we provide a 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.

### A.1 Setup and Notation

Let:

- •
$\mathcal{P}$ be the space of bare prompts $P^{\prime}$.
- •
$\mathcal{H}$ be the space of reasoning traces $H$.
- •
$r:\mathcal{P}\to[0,R_{\max}]$ be the reward random variable obtained by sampling $P^{\prime}\sim\pi(P)$ and generating an image $I=f_{\phi}(P^{\prime})$.
- •
$r_{H}:\mathcal{H}\times\mathcal{P}\to[0,R_{\max}]$ be the reward when first sampling $H\sim\pi_{H}(P)$, then $P^{\prime}\sim\pi(P\,|\,H)$, and finally $I=f_{\phi}(P^{\prime})$.
- •
All expectations and variances are taken over the joint sampling of $H$ and $P^{\prime}$.

### A.2 Law of Total Variance

By the law of total variance,

$$ $\mathrm{Var}\bigl{[}r(P^{\prime})\bigr{]}=E_{H}\bigl{[}\mathrm{Var}[r(P^{ \prime})\mid H]\bigr{]}+\mathrm{Var}_{H}\bigl{[}E\bigl{[}r(P^{\prime})\mid H \bigr{]}\bigr{.}]$ $$

Since variances are nonnegative, we immediately have:

###### Theorem A.1 (Variance Reduction) .

$$ $\mathrm{Var}\bigl{[}r(H,P^{\prime})\bigr{]}=E_{H}\bigl{[}\mathrm{Var}\bigl{[}r \mid H\bigr{]}\bigr{]}\;\leq\;\mathrm{Var}\bigl{[}r(P^{\prime})\bigr{]}.$ $$

###### Proof.

By definition,

$$ $\mathrm{Var}\bigl{[}r(H,P^{\prime})\bigr{]}=E_{H}[\mathrm{Var}[r\mid H]]$ $$

and since

$$ $\mathrm{Var}[r(P^{\prime})]=E_{H}[\mathrm{Var}[r\mid H]]+\mathrm{Var}_{H}[E[r \mid H]],$ $$

the nonnegativity of $\mathrm{Var}_{H}[E[r\mid H]]$ yields the result.
∎

###### Theorem A.1 (Variance Reduction) .

###### Proof.

### A.3 Sample Complexity Improvement

Variance directly impacts the number of samples required to estimate the expected reward within a given accuracy. Consider estimating the expected reward $\mu=E[r]$ by drawing $N$ independent samples $\{r_{i}\}$. By Chebyshev’s inequality,

$$ $\Pr\Bigl{(}|\hat{\mu}-\mu|\geq\varepsilon\Bigr{)}\;\leq\;\frac{\mathrm{Var}[r] }{N\varepsilon^{2}}.$ $$

Thus, to guarantee
$\Pr(|\hat{\mu}-\mu|\geq\varepsilon)\leq\delta$,
we require

$$ $N\;\geq\;\frac{\mathrm{Var}[r]}{\varepsilon^{2}\,\delta}.$ $$

Applying Theorem [A.1](https://arxiv.org/html/2505.17540v1#A1.Thmtheorem1), using reasoning reduces the required sample size:

$$ $N_{\text{reasoning}}\;=\;\frac{\mathrm{Var}[r(H,P^{\prime})]}{\varepsilon^{2} \delta}\;\leq\;\frac{\mathrm{Var}[r(P^{\prime})]}{\varepsilon^{2}\delta}\;=\;N _{\text{bare}}.$ $$

### A.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.

## Appendix B Experiment Setting

Table [5](https://arxiv.org/html/2505.17540v1#A2.T5) 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 $\varepsilon$ to 0.2 and the KL penalty coefficient $\beta$ to 0.04 with a group size of 4. The FLUX.1 model operates at a resolution of $512\times 512$, with 50 diffusion steps and a classifier-free guidance (CFG) scale of 3.5. In joint training, we balance ImageReward and VLM-Reward with equal weights (0.5 each), and constrain prompt lengths between 15 and 77 tokens. Training is conducted using 8 devices with a per-device batch size of 4, a learning rate of 2e-6, gradient accumulation steps of 2, and a total of 3 epochs.

**Table 5: Key Parameters for GRPO and T2I Model.**
| Config | Symbol | Value |
| --- | --- | --- |
| GRPO Config |  |  |
| Clipping range | $\varepsilon$ | 0.2 |
| KL penalty coefficient | $\beta$ | 0.04 |
| Group size | $G$ | 4 |
| FLUX.1 Config |  |  |
| Image resolution | $H\times W$ | $512\times 512$ |
| Diffusion steps | $T$ | 50 |
| CFG scale | $\lambda_{\text{cfg}}$ | 3.5 |
| Joint Training Parameters |  |  |
| ImageReward weight | $\alpha$ | 0.5 |
| VLM-Reward weight | $\gamma$ | 0.5 |
| Min prompt’s length | $L_{\min}$ | 15 |
| Max prompt’s length | $L_{\max}$ | 77 |
| Device number | - | 8 |
| Per device batch size | $B$ | 4 |
| Learning rate | $lr$ | 2e-6 |
| Gradient Accumulation | - | 2 |
| Epoch | - | 3 |

## Appendix C Ablation Study

#### Ablation Study on Reasoning.

Table [6](https://arxiv.org/html/2505.17540v1#A3.T6) presents an ablation study on the GenEval benchmark to assess the impact of reasoning in our reinforcement learning framework. The FLUX baseline, integrating 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 a more substantial improvement, pushing the overall score to 0.72. Notably, categories that require more complex semantic understanding—such as "Colors" (from 0.83 to 0.87) and "Attribute binding" (from 0.46 to 0.53)—benefit the most. These results demonstrate that step-by-step reasoning helps the model better decompose and interpret textual prompts, thereby enabling more accurate and faithful image generation.

**Table 6: Ablation study of reasoning on the GenEval benchmark.**
| Method | Single | Two | Counting | Colors | Position | Attribute | Overall $\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  | object | object |  |  |  | binding |  |
| 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 |
| 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 |

#### Training Dynamics.

Figure [6](https://arxiv.org/html/2505.17540v1#A3.F6) presents the reward curve during reinforcement learning of the RePrompt. We observe a stable and monotonically increasing trend in the reward, demonstrating that our reward model provides a reliable and effective supervision signal throughout training. The absence of sharp fluctuations or reward collapse suggests that our RL setup maintains stable policy updates. This aligns with the observed downstream improvements, confirming that reward-guided prompt refinement effectively enhances compositional alignment in generated images.

Figure: Figure 6: Training curve of our RePrompt during reinforcement learning. The curve shows steady reward improvement, indicating stable training dynamics and effective reward signal.
Refer to caption: extracted/6470159/Figure/line.png

#### Ablation Study on Visual Reasoning Rewards.

To investigate the effectiveness of different visual reasoning reward signals used in our reinforcement learning–based reprompting strategy, we conduct a detailed ablation study on the GenEval benchmark. As shown in Table [7](https://arxiv.org/html/2505.17540v1#A3.T7), our method consistently improves over the FLUX baseline 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 VLM-Reward show noticeable gains over the +Qwen2.5 3B baseline, particularly in Colors (0.88 vs. 0.81 with ImageReward) and Counting (0.69 vs. 0.63 with VLM-Reward), indicating that each reward captures complementary aspects of visual faithfulness. However, their standalone performances are still limited in challenging tasks like Position and Attribute Binding. Notably, our ensemble reward formulation—which combines both ImageReward and VLM-based feedback—achieves the best overall performance (0.72), with consistent improvements in Counting (+0.08), Position (+0.06), and Attribute Binding (+0.05) compared to the +Qwen2.5 3B baseline. These are precisely the categories that require stronger compositional understanding and spatial reasoning, underscoring the strength of our composite reward design in driving semantically aligned image generation.The ablation confirms that each reward contributes uniquely, and their integration enables more holistic supervision, leading to substantial gains in compositional image-text alignment.

**Table 7: Ablation study of rewards on the GenEval benchmark.**
| Method | $\alpha$ | $\gamma$ | Single | Two | Counting | Colors | Position | Attribute | Overall $\uparrow$ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  |  |  | object | object |  |  |  | binding |  |
| 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 |
| R1-Prompter | 1 | 0 | 0.99 | 0.84 | 0.63 | 0.88 | 0.38 | 0.50 | 0.70 |
|  | 0 | 1 | 0.99 | 0.82 | 0.69 | 0.85 | 0.32 | 0.47 | 0.69 |
|  | 0.5 | 0.5 | 0.98 | 0.83 | 0.71 | 0.87 | 0.41 | 0.53 | 0.72 |

#### Training Dynamics with Different Visual Reasoning Rewards.

We analyze the impact of different visual reasoning rewards on training dynamics by plotting training curves under various reward configurations. As shown in Figure [7](https://arxiv.org/html/2505.17540v1#A3.F7), training with a single reward model leads to unstable reward fluctuations and degraded sample quality. This instability arises from the limited supervision capacity of a single reward model, which may overfit to specific patterns or neglect important compositional aspects. In contrast, our proposed multi-reward formulation—balancing diverse reasoning 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.

Figure: Figure 7: Training curve with different visual reasoning rewards. Using a single reward model leads to instability and suboptimal performance during training. In contrast, our balanced reward design—combining multiple specialized reward signals—yields more stable convergence and higher final reward values.
Refer to caption: x5.png

Figure: Figure 8: Qualitative results on 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-guided approach enables more accurate and faithful generations that better match the prompt’s compositional constraints.
Refer to caption: x6.png

## Appendix D More Cases

#### More Qualitative Comparison.

Figure [8](https://arxiv.org/html/2505.17540v1#A3.F8), [9](https://arxiv.org/html/2505.17540v1#A4.F9), [10](https://arxiv.org/html/2505.17540v1#A4.F10) and  [11](https://arxiv.org/html/2505.17540v1#A4.F11) present qualitative comparisons on different types of compositional prompts, including Position, Two-object, Color, and Attribute Binding. Compared to baseline methods such as DELL-E3, our approach produces images that more faithfully adhere to the spatial, numerical, and attribute-based constraints specified in the prompts. For instance, in Figure 7, 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 sometimes 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 confusion. These results demonstrate the effectiveness of integrating reasoning to improve alignment between visual outputs and complex prompt semantics.

Figure: Figure 9: Qualitative results on compositional prompts (Two-object). 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 our method uses explicit reasoning to guide the model in generating semantically correct and compositionally faithful images.
Refer to caption: x7.png

Figure: Figure 10: Qualitative results on compositional prompts (Color). We present qualitative comparisons on prompts involving specific color attributes. While DELL-E3 tends to ignore or misinterpret color constraints, our approach leverages explicit reasoning to ensure accurate color grounding for each object, resulting in more faithful visual compositions.
Refer to caption: x8.png

Figure: Figure 11: Qualitative results on compositional prompts (Attribute binding). We show examples where prompts specify multiple attributes that must be correctly associated with the corresponding objects. Our method utilizes step-by-step reasoning to disambiguate attribute-object bindings, avoiding attribute swaps or omissions that are common in baseline outputs.
Refer to caption: x9.png

## Peer Reviews

confidence: 4
contribution: 1
initial_rating: 2
presentation: 2
questions: Have the authors trained from instruct models as opposed to base models? How much can this method improve the performance?
soundness: 2
strengths: 1. The paper is clearly written and easy to follow.
2. Table 2 shows great transferability of the prompts among different text-to-image models.
3. The ablation study conducted in this paper is very thorough.
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.
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.
2. 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.
3. 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).
4. The authors have also only finetuned from Qwen models and it would be nice to show results from other model families.
5. The authors can consider adding discussions and/or comparisons to the following papers:

Yeh et al. TIPO: Text to Image with Text Presampling for Prompt Optimization. 2024.

Lu et al. Language models as black-box optimizers for vision-language models. 2024.

He et al. Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation. 2024.

---

confidence: 3
contribution: 2
initial_rating: 4
presentation: 3
questions: Please see weaknesses
soundness: 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.
2. 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.
3. 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.
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.
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.

2.  It is unclear how RePrompt would perform on more diverse, stylized, imaginative, or long-form narrative prompts that are common in real-world use. 

3. 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.

4. 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. 

5. 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.

6. 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.

---

confidence: 3
contribution: 3
initial_rating: 8
presentation: 3
questions: - Is human-in-the-loop verification needed to ensure that the reward aligns well the perceptual alignment?

- What is the impact of reasoning length on the generation quality?
soundness: 3
strengths: - The idea of combing LLM reasoning and image-level feedback is novel and promising. 

- 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.

- 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. 

- The paper is well presented and the reproducibility is good.
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).
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.

- 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"

---

confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
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
soundness: 3
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.

- 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%).

- 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.

- 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.
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.
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.

- **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.

- **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."

- **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.


Decision: Accept (Poster)