GoT-R1: Unleashing Reasoning Capability of Autoregressive Visual Generation with Reinforcement Learning
Authors: Chengqi Duan
Rongyao Fang
Yuqing Wang
Kun Wang
Linjiang Huang
Xingyu Zeng
Hongsheng Li
Xihui Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Multimodal Large Language Model
Reinforcement Learning
Visual Generation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that specify multiple objects with precise spatial relationships and attributes. Effective handling of such prompts requires explicit reasoning about the semantic content and spatial layout. We present GoT-R1, a framework that applies reinforcement learning to enhance semantic-spatial reasoning in autoregressive visual generation models. Leveraging the natural affinity between autoregressive architectures and sequential reasoning, our approach builds upon the Generation Chain-of-Thought framework to enable models to autonomously discover effective reasoning strategies beyond predefined templates. To achieve this, we propose a dual-stage multi-dimensional reward framework that leverages MLLMs to evaluate both the reasoning process and final output, enabling effective supervision across the entire generation pipeline. The reward system assesses semantic alignment, spatial accuracy, and visual quality in a unified approach. Experimental results demonstrate significant improvements on T2I-CompBench and GenEval benchmark, particularly in compositional tasks involving precise spatial relationships and attribute binding. GoT-R1 advances the state-of-the-art in autoregressive image generation by successfully transferring sophisticated reasoning capabilities from language models to the visual generation domain. Code is available at https://github.com/gogoduan/GoT-R1.

Published as a conference paper at ICLR 2026
GoT-R1: UNLEASHING REASONING CAPABILITY OF AUTOREGRESSIVE VISUAL GENERATION WITH REIN-
FORCEMENT LEARNING
Chengqi Duan
Rongyao Fang
Yuqing Wang
Kun Wang'
Linjiang Huang*
Xingyu Zeng
Hongsheng Li²
Xihui Liu
'HKU MMLAB
CUHK MMLAB
*Sensetime "Beihang University
SUAT
duancg248connecce hk,
xihuiliuêeee hku.hi
ABSTRACT
Visual generation models have made remarkable progress in creating realistic im- ages from text prompts, yet struggle with complex prompts that specify multi- ple objects with precise spatial relationships and attributes. Effective handling of such prompts requires explicit reasoning about the semantic content and spatial layout. We present GoT-RI, a framework that applies reinforcement learning to enhance semantic-spatial reasoning in autoregressive visual generation models. Leveraging the natural affinity between autoregressive architectures and sequen- tial reasoning, our approach builds upon the Generation Chain-of-Thought frame- work to enable models to autonomously discover effective reasoning strategies beyond predefined templates. To achieve this, we propose dual-stage multi- dimensional reward framework that leverages MILMs to evaluate both the rea- soning process and final output, enabling effective supervision across the en- tire generation pipeline. The reward system assesses semantic alignment, spa- tial accuracy, and visual quality in a unified approach. Experimental results demonstrate significant improvements
T21-CompBench and GenEval bench- mark, particularly in compositional tasks involving precise spatial relationships and attribute binding. GoT-RI advances the state-of-the-art in autoregressive image generation by successfully transferring sophisticated reasoning capabili- ties from language models to the visual generation domain. Code is available https: http//gittuie com http://ooouuunoT-R..
INTRODUCTION
Visual generation (Podell et al. 2023 Ramesh et al. 2022 Saharia et al., 2022 Esser et al. 2024
Nichol et al.) 2021 Labs 2024 Rombach et al. 2022) has witnessed great advances in recent years, enabling the creation of diverse and realistic visuals from natural language descriptions. Despite their impressive capabilities, these models often struggle with complex and compositional prompts that specify multiple objects with precise spatial relationships and attributes (Huang et al. 2025)
This limitation stems from their direct mapping from text embeddings to visual features without explicit reasoning of the compositional structure of the desired scene. The Generation Chain-of- Thought (GoT) (Fang et al., 2025) framework tackles this challenge by introducing an intermediate semantic-spatial reasoning process that decomposes complex prompts into explicit object descrip- tions with location coordinates before image generation, significantly improving compositional fi- delity. While GoT was originally designed for diffusion models, autoregressive architectures offer natural advantages for sequential reasoning tasks due to their step-by-step token generation process. Nevertheless, GoT's reasoning capability-regardllee fine-tuning with human-defined templates, limiting the model's '' ability to autonomously discover more effective reasoning strategies. As shown in Fig. [ we observe that GoT-generated reasoning chains can be unfaithful to the prompt despite following templates well.
of architecture constrained by supervised
"quual Contribution Corresponding Author

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Published as a conference paper at ICLR 2026
Image Generation before
Process
Image Generation after RL.
Promg:
Promet:
Aluticily en Lande
busstt lift cand
A cundle ferne Anth bi www..couutii cutolases
Generatio Chaloel
(GoT)
Genaraied
Image Proinet
Gener Generation Chain Thought (GoT) The image taters butterly iuSecuiisaae ant caste (10s pocgyRR 20r) anddoood butsurty iocatd the left of the carck. stader of orage black wntwe itss wings aee pnodeerant anrraally oragge with amocuse blik and puttotl's body skenie und sopaented with the right the butterly its in condle hounad in sirce giss holder.
Genersted
a Iuly Lrasdue edtie dasds
budggaans: The candeo was b crarry depth and novernnt Anoo the circle ats aociadeeyy colter, ertagnasde taae (ou oxtech of supe pale. The bussrtle negs of vorn congre and veloos, sdomed with inticate pattent and sparding ancents that
For reoectionn MM
Roaard
MLUM
Figure I: GoT-RI enhances visual generation through reinforcement learning. This figure demon- strates the improvement from Go GoT-finetuned model (left) to the RL-trained GoTT-RI model (right). The model before RL generates spatially misaligned reasoning process. The RL process enhances the model's 's semantic-spatial reasoning capabilities, as demonstrated by its Generation Chain-of- Thought, leading to a generated image that more closely aligned with the prompt.
In parallel with udvancements in visual generation, recent breakthroughs in language models have demonstrated that reinforcement learning (RL) can significantly enhance chain thoughttt reasoning capabilities. Models like OpenAl ol (OpenAl 2025) and DeepSeek-RI (DeepSeek -Al 2025) show that autoregressive language models can autonomously discover sophisticated reasoning strategies through self-improvement. Recognizing the natural synergy between autoregressive architectures and sequential reasoning, we introduce GoT-R1, framework that adapts these RL advances to enhance semantic-spale -spatial reasoning in autoregressive visual generation.
Extending reinforcement learning to enhance the reasoning abilities of autoregressive visual gen- cration models presents unique challenges. unlike those encountered in code, mathematics, or tra- ditional language tasks. First, designing appropriate rewards for visual generation is particularly challenging, as evaluating visual outputs requires assessing different dimensions: semantic fidelity to the prompt, accurate spatial arrangement of objects, proper binding of attributes to entities, co- herence, and aesthetic quality. Second, optimizing solely on result rewards is suboptimal as it leaves the reasoning process unsupervised, potentially creating misalignments between the prompt, reason- ing chain, and final image. Without explicit process supervision, the model may generate visually coherent but compositionally incorrect images, or fail to translate well-planned reasoning into accu- rate visual generation. 'Therefore, effective reinforcement learning for visual generation requires a comprehensive reward framework, evaluating both the reasoning process and the final output.
To address these challenges and inspired by the strong visual understanding and reasoning capabil- ities of multimodal large language models (MLLMs) (Bai et al., [023]] Liu et al.] [024b] OpenAT] 2025 Wang et al. 2025a). we leverage an MLLM-based base model for visual generation and pro- pose a dtal-stage Reinforcement Learning framework with unified MLLM-based multi-dimensional rewards. Our base generation model is an auto- regressiee unified MLLM which takes text prompts as input and outputs the reasoming chain followed by a sequence of image tokens. Our reward model evaluates both the reasoning process and the final image output through a comprehensive set of ward signals: (1) crompt-to-reasoning semantic alignment, which assesses how well the reasoning chain captures the textual content; (2) compt-to-reasoningg spatial aligument, which evaluates the fidelity of planned spatial arrangements; (3) reasoning-to-ing to--imagg alignment, which measures how faithfully the generated image reflects the planned reasoning; and (4) prompt-to-image alignment, which evaluates the overall quality and compositional accuracy of the generated image.
We leverage MLLMs as reward models due to their ability make nuanced judgments about text-image correspondence that align well with human assessments. We also enhance MLLMs' spatial evaluation capability by transforming bounding box coordinates into visualized bounding spatial re-
boxes drawn on blank canvas, improving the reliability of the prompt-to-reasonin ward. Through careful reward design and the adoption of Group Relative Policy Optimization (GRPO) (DeepSeek-Al) 2025). GoT-RI enables models to autonomously discover effective rea- soning strategies for complex visual scenes. Experimental results demonstrate significant improve- ments over the baseline model on T21-CompBench and Geneval, advancing the state of composi- tional image generation. Figure| [ illussrraee how GoT-RI substantially improves the handling of compositional prompts. In summary, our main contributions are:
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We propose GoT-RI, framework that enhances the semantic-spatial reasoning abilities for autoregressive visual generation by reinforcement learning, enabling models to discover effective reasoning strategies autonomously beyond predefined patterns.
We design a comprehensive dual-stage multi-dimensional reward framework that evaluates both the intermediate reasoning process and final visual output from multiple perspectives, addressing the unique challenges of reinforcement learning for visual generation.
We demonstrate significant performance improvements on the T2I-CompBench (Huang et al. al. (023)) and GenEval (Ghosh et al. 2023), particularly in compositional tasks requiring precise spatial relationships and attribute binding.
2 RELATED WORK
Text- Visual Generation
Recent advancements in text-driven visual generation have been dominated by two main paradigms: diffusion models and autoregressive approaches. Diffusion models (Saharia et al., 2022 Rombach al. 2022; Nichol et al. 2021 Ramesh et al., 2022 Zhang Xie et al. (024) have demonstrated remarkable success generating high-fidelity images from text prompts by iteratively denoising an initial noise map.
ct al. 2023 Podell Podel et al. Labs]]
Autoregressive approaches (Sun et al. 2024a Li et al. 2024; Tian et al.) [024] Han et al.) 2024]
Wang et al. [024c] Yu et al.) 2an4]Wang et al. 2024b] Fang al. [023]] Wang et al. (025b). on the other hand, typically treat image generation as a sequence modeling problem. They often represent images as sequence of discrete visual tokens (e.g., from a VQGAN) or patches and generate them element by element, commonly using large transformer architectures conditioned on textual input. Despite continuous improvements in generation quality, these methods still struggle with complex scenes involving complex text understanding, precise spatial relationships and attribute binding among multiple objects. Several studies have attempted to leverage large language models to enhance image generation capabilities. Models such as Chameleon (Team] 2024), Emu3 (Wang) iet al. (2024)), and Janus (Wu et al., 2024] Chen et al.] 2025) explore unified architectures for visual understanding and generation. However, these approaches have yet to demonstrate that reasoning capabilities effectively translate to improved generation quality. Recently, GoT (Fang et al.) 2025) introduced explicit semantic-spatial reasoning into image generations.
Multimodal Large Language Models Multimodal Large Language Models (MLhMs)(Achiam) al. 2023 Bai et al. 2023 OpenAl 2025) integrate vision encoders with LLMs, demonstrating
strong visual understanding, sophisticated reasoning, and semantic analysis. Advanced MLLMS further enhance spatial understanding by grounding textual concepts to image regions(Liu et al,, 2024b Peng et al.) 2023) Fang et al., 2024). However, despite unification attempts (e.g.. Janus (Wu ct al. 2024)) and models incorporating generation (e.g., Chameleon (Team] 2024). Emu2 (Sun et al, 2024b)), there remains significant disconnect between understanding and generation capabilities. The rich semantic and spatial reasoning abilities of MLLMs are not yet fully leveraged in the gen- eration process, as seen in models that generate images but may not fully utilize explicit semantic- spatial reasoning for synthesis.
Reinforcement Learning for Reasoning Reinforcement Learning (RL) has emerged as a power- ful approach for enhancing reasoning capabilities in large models. The success of OpenAl ol (Ope- how RL can significantly improve reasoning in language models. A notable algorithm contributing to some of these advancements Group Relative Policy Optimization (GRPO) (Shao et al., 2024).. GRPO is an efficient rein- forcement learning technique that enhances policy learning by evaluating and normalizing rewards among a group of sampled candidate outputs from the model, eliminating the need for a separate critic model. Recent work has extended these techniques to multimodal domains. (Chen et al. Deng cet al. 2025 Liu et al., 2025 Yang et al. 2025 Zhang et al., 2025) Vision-RI (Zhan et al. 2025) applies rule-based RL to enhance object localization in vision- language models without specialized reward models, using criterion-dre driven reward functions that evaluate completions based on visual feedback. Concurrent to work, T21-R1 (Jiang al. 2025) introduces BiCoT-GRPO to jointly optimize semantic- and leven-level Chain-of-1 Chain-of-Thought reasoning for image generation, incor- porating diverse vision experts as reward models.
nAl 2025) and DeepSeek-R1 (DeepSeek-AT, 2025)demonster
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Reward: D8
0.2
Genertte Images
Prompe
[A [eb the left ricrowaed
CRPO Brwarde:
CoT:
MLLM Reward Model
Group Computation
Reard Model
GoT... 137 12211//.5522005555 765
Tokons)
[Bampts]] L
Advntapec
Autoregressive Unified MLLM
GoT-R1
bes nead to mecrowawee)
Figure 2: The GoT-R1 framework illustrating the reinforcement learning process with Group Rela- tive Policy Optimization (GRPO). Left: Overview of the candidate sampling and initial evaluation stage, where diverse reasoning chains (GoT) and corresponding image tokens are generated from an input prompt, with an MLLM-based reward model providing preliminary scoring, Right: Detailed illustration of how MLLM-base rewards and advantages facilitate model updates via GRPO.
METHOD
In this section, we present the details of our GoT-RI framework. We first review the prerequisite knowledge including the Generation Mfain-of-Though (GoT) paradigm and Group Relative Pol- icy Optimization (GRPO) algorithm in Section 3.1 Then, describe GoT-RI framework in Section 3.2. including the network architecture and training strategy. In Section 3.3 we elaborate on our MLLM- based dual-stage multi-dimensional reward design. The reward system thoroughly evaluates the alignment between prompt, reasoning, and generated image to provide comprehensive supervision signals for effective reinforcement learning.
3.1 PRELIMINARY
Generation Chain-of- Thought (GoT) Generation Chain-of-Thoure (GoT) (Fang al. 2025 is a paradigm that transforms visual generation through an explicit visual-semantic thain-of-thoughe reasoning process before outputting images. Unlike conventional text-to-image generation methods that directly map text embeddings to visual features, GoT decomposes complex prompts into a reasoning chain with both semantic descriptions and spatial coordinates. For example, given the prompt "A dog and a cat playing together". a GoTT reasoning chain might include "a playful brown dog" with coordinates (100, 200), (350, 450) and "an orange tabby cat" at (400, 250), (650, 500), specifying both semantic attributes and spatial positioning of each object. This explicit chain-of- thought reasoning enables precise control over object attributes, spatial arrangements, and inter- object relationships, significantly improving compositional fidelity in the generated images.
In order to enable reasoning abilities of the generation model, GoT constructs large-scale training data with annotated reasoning chains following hand-crafted templates. The GoT framework is trained with the annotated data in a supervised manner to generate reasoning chains and images. However, this approach is inherently limited by the hand-crafted and fixed reasoning templates in training data, preventing the model from discovering more effective reasoning strategies. Moreover, the GoT framework trained with supervised fine-tuning tends to generate template-conformet but sometimes unfaithful reasoning chains, which can bottleneck subsequent visual generation.
Group Relative Policy Optimization (GRPO) Group Relative Policy Optimization (GRPO) is proposed by DeepSeck-RI (Shao et al., 2024) to incentivize reasoning capabilities of large language models. is an efficient RL algorithm that eliminates the need for a separate critic model. For cach question q, GRPO samples a group of G outputs {o:)., from the current policy Ta These outputs are evaluated using reward functions to obtain individual rewards {ra].e The advantage for each sample is computed by normalizing the rewards within the group:
4; 7; mean((ry)
(1)
std (1r(()))))))
The policy is then updated by optimizing the following objective:
JGRPO(0) = E
1 G
(2)
min
G
1=1
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Reasoning Semantic Reward Ryem
Generation Prompt:
Prompt-Image Reward Rp
bee next to a microwave
Prompt:
Prompt:
bee next to microwave
A bec next to a microwave
Alignment
GoT Reasoning: e. (/ ys]... microwave (a.
Alignment
GoT (without boxes): bee vn mkrrowae
Generated Image:
Reasoning Spatial Reward Rspa
Bassoning-lmag Reward Rgi
Prompt:
GoT (boxes):
bee next to microwave
Alignment
loU Alignment
GoT (boxes):
Pub. Al
Generated Image
Canuscing
Figure 3: Overview of our MLLM-based dual-stage multi multi-dimensional reward framework. The diagram illustrates MLLM-based rewards assessing the intermediate GoT's semantic and spatial fidelity to the prompt, as well as the final image's alignment with both the prompt and the GoT.
where r0) = To(oulg) is the probability ratio, is the clipping parameter, and ß controls the (0:1g)
strength of the KI. divergence penalty from reference policy कh.- This group-based approach provides computationally efficient method for policy optimization while effectively leveraging relative performance differences within each group of samples.
3.2 GOT-R1 FRAMEWORK
GoT-RI builds upon the Generation Chain-of- Chaun-of-Thought (GoT) (Fang et al. 2025) framework for text-to-image generation by introducing reinforcement learning to enhance semantic- reason- ing capabilities. As discussed earlier, while GoT provides a strong foundation for compositional image generation, its effectiveness is limited by predefined reasoning templates in the training data. Our framework addresses this limitation by enabling the model to autonomously discover better reasoning strategies through reinforcement learning while maintaining the end-to-end optimization.
Network Architecture We adopt a unified MLLM that jointly models text and image tokens as our base architecture. For example, Janus-Pro (Chen et 2025) is capable of both visual understanding and generation tasks, processing images as discrete tokens alongside text with joint autoregressive modeling. This architecture allows us to generate textual reasoning chains and visual outputs in an end-to-end manner, enabling comprehensive optimization of the entire generation process.
Training Strategy Our base model has been trained on text-to-image generation task without chain-of-thought reasoning processes. To incentivize the reasoning abilities, our training process consists of two stages: In the first stage, we fine-tune the pre-trained model with reasoning chain and generated image annotations from GoT dataset. This stage of SFT establishes the basic capa- bility to generate templated reasoning chains before generating image tokens, providing a strong initialization. In the second stage, apply reinforcement learning to guide the model explore free-style and more effective reasoning chains. For each prompt P, we sample N different reasoning chains and corresponding images. These samples are then evaluated using our multi-dimensional reward function, which assesses both reasoning quality and generation fidelity. The model parame- ters are updated using GRPO encourage high-reward reasoning strategies and generated images, and discourage the low-reward ones. The specific design of our reward function, which addresses the unique challenges of evaluating visual reasoning quality, is detailed in the following subsection.
3.3 MLLM-BASER DUAL-STAGE MULTI- BILLT-DDMMNNIONAL REWARD
The GoT-RI generation framework is composed of two stages: prompt reasoning chain gener- ation, and reasoning chain to image generation. A straightforward integration with reinforcement learning would be to apply end-to-end reward based solely prompt- pomppt-maaee alignment. However, without explicit constraints on the intermediate reasoning process, the reasoning chains may become unfaithful the prompt inconsistent with the final image, undermining the interpretability and
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controllability of the generation pipeline. To guide the model toward faithful and consistent gener- ation, we design a dual-stage reward mechanism with both result and intermediate process supervi- sion. Specifically, we define four categories of rewards: (1) Rp, measures the alignment between Prompt and generated Image, (2) R measures the faithfulness of Reasoning process to input Prompt, (3) Rat measures the fidelity of generated Image to Reasoning process and (4) Rurs which uses HPS v2.1 (Wu et al. 2023) to improve generation quality. For the prompt-to-reasonnng alignment reward RPR, we further decompose the reward into two distinct aspects-semanee semantic re- ward Reem and layout reward Rapa- to ensure both the semantics and spatial arrangement in the reasoning process faithfully reflect the input prompt. All rewards are scaled to range [0,1]. We define total reward Restal the product of individual rewards:
Riotal = Rp1 RpR * RRI * RHPS = Rp1 * (Reem + Raya) RRI * RHPS
(3)
2
MLLMs are uniquely well-suited as reward models this context due to their strong cross-modal understanding and reasoning capabilities. Trained on large-scale image-text pairs, MLLMs can provide unified, interpretable, and fine- grained evaluations for both reasoning chains and generated images across diverse aspects such semantic consisteney and spatial arrangement. This makes them ideal for reward functions in reinforcement learning settings, where conventional metrics often fall short in providing nuanced multi-dimensional feedback. The rewards are demonstrated in Fig. 3
Prompt-Image Reward (Rp)) The most intuitive reward design is the overall alignment between the input prompt and generated image. Leveraging the outstanding image understanding capabilities of MLLM, we utilize to perform multi-dimensional evaluations of the final generated image, assessing whether it aligns with the composition (objects, attributes, layout etc.) specified in the prompt. The MLLM takes the input prompt and the generated image as input and predicts a discrete score ranging from 10 where 10 stands for the best.
Prompt-Reasoning Semantic Reward (Rsem) To assess semantic consistency between the in- put prompt and generated GoT reasoning, we leverage MLLMs evaluate each GoT in terms of missing elements (attributes), internal contradictions, logical consistency, and formatting quality. Specifically, the GoT reasoning along with the input prompt are input MILM assess the rea- soning chain from four dimensions with a score from 0 to 10: 1) Completeness: Does the reasoning chain include all concepts mentioned in the prompt? 2) Faithfulness: Does it introduce any content that contradicts the prompt? 3) Consistency: Is the reasoning logically aligned with the described scene? 4) Clarity: Is the content coherent and properly formatted?
Crompt-Reasoning Spatial Reward (Rspa) To evaluate the correctness of spatial planning by the reasoning chain, our MLLM reward model assesses whether the GoT object co- ordinates follow the spatial relationship (c.g., "left" or "top")) from the prompt. However, lightweight LLMs or MILMs exhibit limited sensitivity to bounding box coordinates and re- lationships between different spatial locations.
Prompt
A bee next micrewave
MLLM Rewordd Model
GoT Spatial Reward
GoT Ispatial::
120.,7709337722
Figure 4: Prompt-Reasonie Spatial Reward Rype process. For robust spatial evaluation, the MLLM assesses bounding boxes rendered on an image from the GoT's textual coordinates, rather than processing the coordinates directly as text.
To bridge this capability gap. we propose an innovative MLLM-bases layout evaluation ap- proach based on critical observation: MLLMs exhibit superior spatial comprehension when processing visual data compared to coordinates in text form. Therefore, we convert textual coordinates into images by rendering corresponding bounding boxes blank canvas. With this visual format, the MLLM demonstrates significantly better spatial understanding and can provide clear and accurate scoring of the reasoning chain's spatial correctness. Figure
illustration of this process.
Reasoning- Reward (RRr) During reinforcement learning, the model can occasionally generate images that deviate from its planned reasoning path. To further ensure that the GoT reason- ing is faithfully reflected in the generated image, our framework incorporates an alignment reward between the GoT reasoning process and the generated image. Specifically, planned in the GoT to appear at the corresponding location in the image. An MLLM is used to iden- tify the location of each object in the generated image, yielding grounded bounding boxes denoted
expect each object
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Blmage, For every object specified in GoT, define its alignment reward as the Intersection
Union (IoU) between the planned bounding box (BCOT) and its grounded counterpart in the image (Blnage). The overall reward R is then calculated as the average IoU across all N objects.
EXPERIMENT
4.1 TRAINING SETTINGS
We trained models separately based on Janus-Pro-IB and Janus-Pro-7B (Chen 2025). Our training process contains two stages: Pretraining on GoT-T2I dataset and online GRPO rein- forcement learning with constructed prompt set. Specifically, We pretrain our model with LAHR- GoT (Schuhmann et al. (022), JourneyDB-Gon D-TT (Sun et al., 2023) and FLUX-GoT datasets for 70000 steps, followed by 1000 steps of GRPO. Our constructed dataset for GRPO consists of prompts from T21-Compbench training dataset and Laion-Aesthetics. When training with GRPO, the overall reward is calculated as the product of individual rewards described in Section 3.3 We employ low-rank adaptation (LoRA) (Hu et al. 2022) to efficiently update the trained model, with rank and lora alpha set to 32. Both phases operate end-to-end. our GRPO training setup, we adopt a batch size of 8, learning rate of 10-5, and employ cosine learning rate schedule. For each input, we sample a group of N = 16 candidates and set both the text and image temperatures to 1.0. As the reward model, adopt Qwen2.5VL-77 (Bai et (025). The loss computed over the entire generated output sequence. GRPO training was conducted on 8 NVIDIA L40S GPUs in 48 hours.
4.2 QUANTITATIVE EVALUATION
In this section, we present the quantitative results of our model numerous benchmarks.
T21-CompBench Desults.Tablee [ shows our model's performance compared to three categories of approaches: diffusion models with frozen encoders, two-stage layout-guided models, and auto- regressive models enhanced with LLMs/MLLMS. GoT-R1-7B achieves state-of-the-art results, ob- taining the highest scores five six evaluation categories with up to 15% improvement after 1000 GRPO fine-tuning steps. Our model shows notable performance on the Complex compositions cat- egory, while GOT-RR-IB outperforms larger models like Janus-Pro-7B: in several categories.
GenEval Results. On the GenEval benchmark (Table 2 GoT-R1-7B establishes new state-of- the-art with an overall score of 0.75, representing improvements over the base Menus-Pr-GGoT-P model. The performance gains are particularly notable in compositional abilities: two-object gener- ation improves from 0.69 to 0.94, and attribute binding advances from 0.43 to 0.68. These results demonstrate the effectiveness of combining structured reasoning with reinforcement-guudee opti- mization for complex visual generation tasks.
General Image Quality Results. To assess performance beyond compositional benchmarks, evaluate on the COCO 2014 validation set (30k images) using standard image quality metrics. Table 4ssows model achieves improvements in CLIP Score and Aesthetic Score. Human evaluation 300 randomly selected prompts demonstrates strong preference for GoT-RI-7B (77%) over baseline models, indicating enhanced overall generation quality alongside compositional accuracy.
4.3 QUALITATIVE EVALUATION
Figure S presents a qualitative comparison among the base model Janus-Pro-7B, the GoT- finetuned model Banus-Pro-7B-Gon GRPO-enhanced model GoT-RI-7B. We show- case examples generated from compositional prompts involving multiple attributes, rela- tive spatial relationships, and object numeracy. While the GoT-finetuned model produces images of higher quality than the base model, it still strug- gles with complex compositional generation. In contrast, GoT-Ri-7B demonstrates stronger prompt alignment, accurately reflecting even unnatural prompts its generations. In addition, GoT-R1-7B generates detailed and aesthetically appealing visual contents. These gains are largely attributed to our MLLM-bal --sedd reward design, which guides the model to optimize both semantic and spatial alignment across the GoT reasoning process and output image. By leveraging fine-grained evalua-
Table 4: General image quality evaluation on
COCO 2014 validation set.
and our
Model
CLIP
Aesthetic?
Humant
Janus- ProssPPr-7BB
28.67
4.88
9%
29.97
5.19
14%
GoT-R1-7B
31.83
5.41
77%
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Table 1: Quantitative evaluation of text-to-image generation T21-CompBench. GoT models refer
to Janus-Pro Prous-Pro finetuned using the GoT framework, while GoT-RI models denote further training via
GRPO on the GoT-finetuned checkpoints. GoT-RI models are evaluated under guidance scale 5.
Model
Color
Shape
Texture
2D-Spatial
Non-Spatial
Complex
Diffusionn Models
SD-v1.5 Rombach et al.
2022
0,3758
0.3713
0.4186
0,1
0.31
0.3047
SD-XL 3D-Xe-base-11 L 1.oee-TTPPPPooo (Podell ef al.
base-
2023
0,5879
0.4687
0.5299
0,2131
0,3119
0.3237
DALLE-3 Betker et al
2023
0,7785
0.6205
0,7036
0,2865
0,3003
0,3773
Stable v3 Esser et al
2024
0.8132
0,5885
0.7334
0.3200
0.3140
0.3771
FLUX.1
abs 20724
0.7407
0,5718
0.6922
0,2863
0.3127
0.3703
Layout Guided Two-stage Models
Ranni Feng et al.
2024
0.6893
0.4934
0.6325
0.3167
LayouGGP1 lama 7B (Feng al. 2023
0.3296
0,3654
0,3982
0.1443
0.2990
0.2768
Auto-regressivee Models
Emu3 (Wang
2024a
0.7544
0.5706
0.7164
Janus-Pro-TH 1B (Chen al. 2025
0.3411
0.2261
0.2696
0.0968
0.2808
0.2721
Janus-Pro-IB-Cor
0.6336
0,4456
0,5621
0.2140
0.3070
0.3490
GoT-RI-IB
0.7632
0.5174
0.6589
0.2674
0.3101
0.3749
Janus-Pro-7B Chen 2025
0.6359
0.3528
0.4936
0.2061
0.3085
0.3559
Janus-Pro-7B-col
0.6551
0.5008
0.5836
0.2457
0.3113
0.3754
GoT-RI-7B
0.8139
0.5549
0.7339
0.3306
0.3169
0.3944
Table 2: Quantitative evaluation on GenEval (Ghosh et al.
2023
Obj.: Object. Attr. Attribution.
Method
Architecture
Overall
Single Obj.
Obj
Counting
Position
Binding
SDv1.5
Rombach et al
2022
Diffissin
0.43
0.97
0.38
0.35
0.76
0.04
0.06
SDv2.1
Diffusion
0.50
0.98
0.51
0.44
0.85
0.07
0.17
SD-XL
Podell al au RUM
Dilfusion
0.55
0.98
0.74
0.39
0.85
0.15
0.23
DALLE-
Rummssh al
6022
Diffasion
0.52
0.94
0.66
0.49
0.77
0.10
0.19
S13 (d=247
Diffusion
0.63
0.67
0.34
LayourGPT
Feng et
1023
Two-stage
0.41
0.97
0.51
0.26
0.56
0.11
0.07
RPG Yang
Two-stage
0.50
0.98
0.66
0,17
0.85
0.07
0.37
Llamorsn SinELEde 7024
Autoregressive
0.34
0.21
0.58
0.07
Chamcleon Teoim
Autoregressive
0.39
LWM (Li 211441
Autoregressive
0.47
0.93
0.41
0.46
0.79
0.09
0.15
SEED-X
(Ge alp 203
MLLM-DDiTusion
0.49
0.97
0.58
0.26
0.80
0.19
0.14
Emui. Gen Wann 2 2.
20244a
Autoregressive
0.54
0.98
0.71
0.34
0.81
0.17
0..1
Wu
Autoregressive
0.46
Jamus Mic 2024
Autoregressive
0.63
0.97
0.59
0.45
0.83
0.53
0.42
GoT (ang 2023
MLLM--Difusion
0.64
0.99
0.69
0.67
0.85
0.34
0.27
Jamus-Poo7B-Gol
Autoregressive
0.48
0.85
0.43
GoT-R1-7B
Autoregressive
0.75
0.99
0.94
0.50
0.90
0.46
0.68
Table Ablation study on reward design. All models trained for 1000 steps using GRPO based
on the Janus- Pro- BB-GoT (Baseline). Evaluations are conducted with guidance scale of 5.
Method
Racm
Raps
RaT
Rp
Color
Shape
Texture
2D-Spatial
Non-Spatial
Complex
Baseline
0.6336
0.4456
0.5621
0.2140
0.3070
0.3490
wRPR
0.7050
0.4671
0.6075
0.2283
0.3089
0.3619
wRa wRer
0.3340
0.2563
0.3940
0.0076
0.2537
0.2488
V
0.7401
0.5066
0.6308
0.2398
0.3076
0.3724
WRygkRpi R RRRr&Rp
0.7289
0.4893
0.6485
0.2557
0.3094
0.3653
/
0.7118
0.4582
0.6243
0.2579
0,3097
0.3583
0.6507
0.4299
0.5913
0.1797
0.3010
0.3452
wRem wR,,
0.7323
0.4729
0.6251
0.2133
0.3094
0.3568
0.7067
0.4685
0.61
0,2419
0.3089
0.3648
GoT-RI-IB
0.7632
0.5174
0.6589
0.2674
0.3101
0.3749
tions from MILM, our reward formulation enables GoT-R1-7B to excel not only visual quality but also in faithfully capturing the intent of complex prompts.
8

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Published as a conference paper at ICLR 2026
Janus-Pro-78
Janu--Poo-7BGoT
GoT-R1-78
Janus Pro
Janus Pro-78-GoT
GoT-R1-78
chac yollow pand
Fax Nurtwryy sreettier with onner arvarppreed by bosd broeger and frdct yrwors
A and grett the boffrecn with hark nelit enda pellow on foor.
Assrase parting as chait trangal like, sanounand greanery
Figure 5: Qualitative comparison among the base model Janus-Pro-75 Pro--Br the GoT-finetuned check- point Kinus-Pro-7B-Gis and our GRPO-enhanced model GoT-RI-7B. Our model demonstrates superior performance prompt alignment and image quality.
4.4 ANALYSIS ON SELF-EXPI.ORED GENERATION CHAIN-OF-THOUGHE
To assess the quality reasoning, we compared the self-explored Generation Chain of Thought from GoT-R1-7B against the predefined GoT of Lanus-Pro-BB-GoT. GPT-4o (Achiam et al. 2023)
evaluated the GoT content for 100 prompts randomly sampled from each of TTTT T2I-CompBench
Color, Spatial, and Complex categories, plus 100 from LAION-5B (Schuhmann et al. 2022). Voting was based on four criteria: relevance to the input prompt, accuracy of object descriptions and bounding boxes, and the clarity and fluency of the text. As detailed in Table GoT-RR-7B's self- explored reasoning is overwhelmingly preferred by GPT-4o across all evaluated categories. This strong preference underscores GoT-RI's ability to autonomously discover and generate superior reasoning paths, key factor contributing to its enhanced compositional generation capabilities.
4.5 ABLATION STUDY ON REWARD DESIGN
We conduct thorough ablation study on our MLLM-based dual- stage multi-dimensional reward and key training settings to validate their contributions. All experiments performed on 121-CompBench, and trained for 1000 steps using GRPO
Table 5: GPT-4o vote results comparing Janus-Pro-7B-G- and GoT-RI-7B on reasoning quality.
Method
Color
Spatial
Complex
LAION-5B
JanTs-Pro-7B-GoT
21
16
29
31
GoT-RI-7B
79
84
71
69
based on the Janus-Pro-IB- in Table and
checkpoint, which serves as our baseline,
Results are displayed
Ablation Study on Reward Design Table revealls both the contribution and limitation of each reward compo- nent. Training with only Rrs yields the best performance among these single-reward variants but still falls short of the full GoT-RI-IB, as the
Table 6: Ablation study on training details. We present re-
sults T21-Compbench evaluated under guidance scale 5.
Method
Color
Shape
Texture
Spatial
Non-Spatial
Complex
Baseline
0.6336
0.4456
0.5621
0.2140
0.3070
0.3490
Sum reward
0.7154
0.4385
0.5608
0.2254
0.3080
03363
evalusted Ray-
0.7166
Conventional rewands
0.5914
0.4844
0.5617
0.1388
0.2936
0.3383
GoT.RI
0.7632
0.5174
0,6589
0.2674
0.3101
0.3749
GoT reasoning process largely bypassed. Relying solely on RPR leads to poorer outcomes, underscoring the necessity of rewarding the final generated image. Furthermore, using only RRJ can detrimental, because the absence of prempi-reasoning reward Rrn results misaligned reasoning process and thus provides harmful guidance image generation. Further experiments

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Published as a conference paper at ICLR 2026
in Table 3 where individual reward components removed from full reward set, reinforce this conclusion. Removing either Rạı or RpI leads to a noticeable degradation in performance. Critically, removing R while retaining Ras once again results in more significant performance decline, as the model attempts to align the image with potentially flawed reasoning. These findings collectively justify the importance of comprehensive reward design that aligns all stages of the generation process.
Ablation Study on RRR Composition Regarding the composition of RPR, we ablate its con- stituents, Asem(pe (pronpt-reasoning semantic reward) and Rapa (prompt-reasoning spatial reward), by training models where only one is active. The results in Table demonstatte their complemen- tary roles: Reen primarily enhances attribute binding. whereas Rapa improves spatial consistency, confirming the value of their combination within Rpn.
Ablation Study on Training Details We further ablate three key settings in our training. In Equa- tion [ the total reward Reotal the product of its individual terms. We evaluate an alternative setting that sums the rewards instead. Moreover, we ablate our novel MLLM layout evaluation ap- proach, where instead of converting GoT layout plans to image for MLLM to assess, Rapa is given by MLLM evaluating GoT layout directly from its textual coordinates. Last but not least, we re- place all MLLM-based rewards with conventional metrics: CLIP similarity for the prompt-image reward and Grounding DINO (Liu et al.) (024c) for the reasoning-imag alignment. The results are presented in Table The findings aftirm the efficacy of our training settings in optimizing GoT--1.
CONCLUSION
In this paper, introduce GoT-RI, novel framework that enhances the semantic-spatial reasoning capabilities of autoregressive visual generation models through reinforcement learning. Building on the Generation Chain-of-Thouglhe paradigm, GoT-R1 empowers models to move beyond predefined templates and autonomously discover more effective reasoning strategies for complex, composi- tional prompts. A key contribution of work is the development of MLLM-based dual-stage multi-dimensional reward framework which evaluates both the intermediate reasoning process and the final visual output, assessing semantic alignment, spatial accuracy, and overall image quality. Our experimental results on the T21-Compeench and GenEval benchmarks demonstrate signifi- cant improvements over existing methods, particularly in tasks requiring precise attribute binding and spatial relationships. GoT-R1 advances the state-of-the-an art and opens new avenues for creating more accurate and contextually aware visual content. We leverage GPT-5 for grammar refinement.
ETHICS AND REPRODUCIBILITY STATEMENT
Our work builds on publicly available datasets such T21-CompBench trainset and GoT datasets, without the use of private or sensitive data. We are mindful that text-to-image generation can be misused for creating misleading or harmful content; thus, our experiments are limited research benchmarks and synthetic evaluation only. To ensure reproducibility, we report all model architec- tures, training setups, and hyperparameters detail (Sections 3-4), and validate robustness through multiple runs (in appendix) and ablation studies. Upon acceptance, we will release code, check- points, and evaluation scripts to support transparent verification and further research.
7 ACKNOWLEDGMENT
This work supported by the National Nature Science Foundation of China (No. 62402406).
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A QUALITATIVE EVALUATION
We present more qualitative analysis on our GoT-RI-7B model in Figure 6 This figure show- cases a comparison of text-to-image generation capabilities among the baseline Janus-Pro-7B. the GoT-finetuned Sanus-Pro-GoT-ia and our GoT-RI-7B model across various prompts, highlighting differences image quality and prompt adherence.
Janus-Pro-78
Janus-Pro-GoT-78
GoT-R1-78
Jenus-Pro-78
Jarus-Pro-GoT-78
GoT-R1-7B
Cardina,,
arridst
and garbah pardday, woiden 100
moderm adlpping conma we with herpe windown, leekrring bad
chamming gardon featuling woodon greent Dusc, dirpurlyiin housoplants, deconel
VeNeEA sndwerhe cancades down mady tsd, Ioreded anvermmment, with
poinding foolurnng bouquet po yol vaso. cocompanicd tabledich
Figure 6: Samples of text-to-image generation by Kanus-Pro-7BB Sanus-Pro-GoT-BB and GoT-RI- 7B.
B ADDITIONAL QUANTITATIVE ANALYSIS
MILM Reward Model Reliability, To validate our reward model choice, we measured correlation with human judgment using Kendall's tau and Spearman's 's tho on 500 T2I-CompBendl prompt- image pairs. Table 7 shows our Qwen2.5VL-7B reward model achieves higher alignment with human preference compared to CLIP Score.
Table 7: Correlation analysis with human judgment for different reward models.
Model
Kendall's tau (r)
Spearman's 's rho (p)
CLIP Score
0.1810
0.2141
Qwen2.5VL-7B (Ours)
0.3147
0.3428
Training Stability. Three independent training runs from different random seeds show consistent performance across all 721-CompBench categories, with variance less than #0.02 as shown in Table confirming the robustness of training process.
Multi-dimenssonal Reward Design. An experiment using only HPSv2 as reward signal demon- strates the importance of our multi-component design. Table shows that while achieving higher HPSv2 scores, the single-reward model exhibits reward hacking with degraded compositional per- formance, confirming that our balanced reward formulation prevents optimization pathologies.

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Published as a conference paper at ICLR 2026
Table 8: Training stability analysis across multiple Cel-Chhmnencch
Category
Run
Run 2
Run 3
Color
0.8139
0.8097
0.8211
Shape
0.5549
0.5610
0.5633
Texture
0.7339
0.7341
0.7450
2D-Spatial
0.3306
0.3398
0.3289
Complex
0.3944
0.3895
0.3937
Table 9: Comparison of single vs. multi-dimensional reward training.
Method
HPSv2 Score f CLIP Score t 2D-Spatial Complex
HPSv2 Only
28.9 27.2
25.3
0.1891
0.2847
GoT-R1 (Multi-reward)
31.83
0.3306
0.3944
C LIMITATIONS
Our approach has several constraints that should be acknowledged.
Position Prediction in Object-Heavy Scenes: The model's ability generate accurate and corre- sponding bounding boxes for each distinct object decreases when prompts contain more than ten items. This can impact the fidelity of very complex scenes.
Inference Latency:
We acknowledge that the explicit generation of the GoT reasoning chain intro- duces additional latency during inference. Our internal benchmarks show this can increase the total generation time by approximately 30 compared to direct generation without the reasoning step.
Reliance on the Reward Model's Capability: The performance of our framework is inherently tied to the power of the MLLM used as reward function. While our results show the current reward model effective, we acknowledge that training GuT-RI with an even more powerful, next-generation MILM could further unlock performance gains. This reliance represents dependency on the progress of external models and is a key avenue for future improvement.
D MLLM-BASED REWARD EVALUATION PROMPTS
We present the prompt used in our paper in Figure g the prompt used for evaluating the semantic consistency between prompt and reasoning chain. Fig- ure [ shows the prompt for evaluating the spatial layout predicted in reasoning chain. Figure TT displays the assessment prompt for prompt-image alignment. Figure illustrate the prompt used for grounding in the reasoning image reward. Figure 12 provides the prompt utilized for comparing reasoning chains with GPT-4o.
Specifically, Figure g details
E REWARD CURVE IN GRPO TRAINING
Figure shows the total reward curve during GRPO training. We rescale the reward values such that the reward at step 0 is mapped to 0, and the reward at step 1000 is mapped to I for better visualization. The reward steadily increases over the 1000 training steps and gradually stabilizes, indicating that the model consistently learns higher-quality reasoning and generation strategies under multi-dimensional reward framework.
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Figure 7: Total reward curve our GRPO stage. We rescale the reward values such that the reward at step is mapped to 0, and the reward step 1000 is mapped to for better visualization.
Human: You are professional image caption evaluator. You will evaluate how well detailed Al-generated caption aligns with brief image prompt.
You will be given:
1. A brief image prompt that describes what should be in the image
2. A detailed caption that was generated based on the brief prompt
Your task is to evaluate if the detailed caption is aligned with and faithful to the brief prompt.
Consider:
Does the detailed caption include all elements from the brief prompt?
Does the detailed caption add elements that contradict the brief prompt?
Is the detailed caption reasonable and consistent with what the prompt describes?
Is the caption coherent and properly formatted?
The score should be from 0 to 10:
0: Completely nonsensical output, messy code, or gibberish that fails function as caption
1-2: Severe misalignment. The detailed caption fails to represent key elements or completely
contradicts the brief prompt
3-4: Poor alignment with significant omissions or contradictions to the brief prompt
5-66: Moderate alignment with some missing elements or noticeable inconsistencies
7-88: Strong alignment with minor inconsistencies additions that don't I contradict the prompt
-9-10: Perfect alignment. The detailed caption faithfully includes all elements from the brief
prompt with uppropriate elaboration
Brief prompt: Prompt
Detailed caption:
Reasoning Chain
Note to only ouput with a dictionary with score this format: ("score": ...)
Assistant:
Figure 8: Prompt for Rsem evaluation.
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Human: Determine objects are arranged described in the prompt by analyzing the image. ORIGINAL IMAGE PROMPT: prompt
COORDINATE SYSTEM EXPLANATION:
The image shows object bounding boxes with names labeled at the top-left corner of each box
SCORING RULES:
Score 8-10 if the objects are shown in the image and their positions MATCH the relationship in the prompt
10: Perfect match with clear relationship
9: Strong match with minor ambiguity
8: Good match with some ambiguity
Score 5-7 if the relationship is partially correct or ambiguous
7: Mostly correct with some misalignment
6: Relationship is ambiguous but leaning toward correct
Borderline case where relationship could be interpreted either way
Score 1-4 if the objects are NOT shown in the image or positions CONTRADICT the rela-
tionship in the prompt
4: Slight contradiction missing one object
*3: Clear contradiction but objects are present
2: Major contradiction missing multiple objects
1: Complete mismatch with the prompt
Please answer in order to: Verify if the objects are shown in the ORIGINAL IMAGE PROMPT. Decide the relationships between objects match what is described in the ORIGINAL IMAGE PROMPT.
Your response MUST be formatted as:
{
"reasoning":
"score":
}}
Output only the dictionary with nothing else.
< Tmage Visualized reasoning chain /Tmage
Assistant:
Figure 9: Prompt for Rapa evaluation.
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Human:
You an expert in visual analysis specializing in compositional accuracy evaluation. Your task is to compare the caption with an image and assess ONLY how well the image matches the described elements, objects, and their relationships. Analyze:
Compositional accuracy: Evaluate all key elements mentioned the caption appear in the image with correct relationships, positioning, and attributes as specified.
EVALUATION CRITERIA:
1. Object Presence: Are the key objects mentioned in the image prompt correctly shown in the image?
2. Spatial Positioning: Are the objects positioned in the EXACT spatial relationships described
in the caption? Pay special attention to positional terms like "on top of," "next to," "inside," "left of," "right of," "behind," "in front of," etc.
Examples of STRICT spatial interpretations:
"Left of means the object must be positioned horizontally the left, not above, below, or on top.
"OO top of" means the object must directly above and touching, not beside below. Compositional accuracy score (0-10):
8-10: Perfect match. Image contains all elements with EXACTLY the spatial relationships described.
-5-7: Minor mismatch. All objects present but with slightly incorrect spatial relationships.
00-4: Major mismatch. Objects present but with completely incorrect spatial relationships, missing key objects.
Caption: prompt
Your response MUST be formatted as:
{{
"description": "ONE sentence describing the image accurately, including the spatial relation- ship observed".
"captiion": : "repeat of the image caption provided".
"reasoning": "ONE sentence explaining the spatial positioning the image EXACTLY matches contradicts the caption", "score":
}}
Output only the python dictionary with nothing else.
Image Generated Image /Tmage Assistant:
Figure 10: Prompt for Rp evaluation.
Human:
Locate the < object >, report the bbox coordinates in JSON format.
Assistant:
Figure 11: Prompt for Rıi grounding.
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Human:
You are an assistant tasked with evaluating two detailed image captions based on given input prompt. Your goal to determine which caption provides better and more accurate descrip- tion of the image, considering the object descriptions and their corresponding positions.
Task: Evaluate the two detailed image captions provided below, based on the given input prompt. Select the caption that is better and more accurate description of an image, con- sidering the object descriptions and their corresponding bounding boxes. The detailed captions includes the bounding boxes of the objects in the image, which are represented as (xl, x2), (yl, y2). (Assume a standard image coordinate system where (0,0) the top-left coreer.. Input Prompt:
prompt Detailed Caption A:
Reasoning Chain A
Detailed Caption B:
Reasoning Chain B
When deciding which caption is better, please consider the following:
Relevance to the Input Prompt: How well does each caption address and align with the original input prompt?
Accuracy of Object Descriptions: Are the objects described correctly and in sufficient detail? Accuracy of Bounding Boxes: Do the provided bounding boxes (x1, x2), (yl, y2) accurately represent the location and extent of the described objects?
Completeness: Does the caption identify and describe the key objects relevant to the input prompt? Does it miss any important elements include irrelevant ones?
Clarity and Coherence: Is the caption easy to understand? Are the object descriptions and their spatial relationships (implied by bounding boxes) presented logically? Naturalness and Fluency: Does the caption read like natural and well-written description? Specificity vs. Generality: Does the caption provide an appropriate level of detail based on the input prompt, or is it too vague or overly specific?
Output Format:
Please provide your response in the following format:
{{
Reasoning: "Your reasoning here".
Selected Caption: "A or B",
}}
Assistant:
Figure 12: Prompt for GPT-4o reasoning chain comparison.


Reviews:
[{"review_id": "r2WBG6AFnS", "reviewer": "Reviewer_Xzzf", "summary": "This paper proposes a reinforcement learning framework for training Generation Chain-of-Thought (GoT) capabilities in image generation. The base model used in this framework is a unified generative understanding model, which leverages the base model's ability to generate text, coordinates, and images. A dual-stage multi-dimensional reward framework is designed and trained using the GRPO algorithm. Experimental results show that the proposed framework delivers significant performance improvements on T2I-CompBench and GenEval.", "questions": "1. Refer to the issues raised in the weakness section.\n\n2. Why is multiplication used between rewards in Equation 3? Summation is generally used now. Experiments are needed to verify this. The curves showing the changes in rewards during training should also be added.\n\n3. Why not utilize the generative understanding of the unified model's own understanding capabilities to obtain rewards, thus avoiding reliance on an additional large multimodal model as the reward model?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper uses reinforcement learning to enhance Generation Chain-of-Thought (GoT) capabilities, which is an interesting and meaningful perspective in the field of multimodal generation.\n2. This paper proposes a dual-stage multi-dimensional reward framework that comprehensively utilizes the prompts, reasoning content, and images in spatial and semantic alignment. This design effectively utilizes the capabilities of the unified generative understanding model.", "weaknesses": "1. The paper does not specify the number of GRPO training steps or the amount of data used. No curves illustrating the GRPO training process are shown.\n\n2. The authors used a model that unifies generative understanding, but did not evaluate the performance of the trained model in multimodal understanding. Why is that? I am very interested in how the multimodal understanding capability changes after training with reinforcement learning on GOT.\n\n3. The ablation experiments shown in Table 4 are puzzling. For example, row 5 (w $R_{PI}$) plays a crucial role, but row 8 (w $R_{RI}$ & $R_{PI}$) with the addition of $R_{RI}$ performs worse, and the penultimate row further weakens the gain. However, adding $R_{sem}$ at the end reverses this and becomes the best performance. The ablation experiment results here are strange, making me doubt the reliability of the experiment.", "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": "xv2VXDQ50e", "reviewer": "Reviewer_hFob", "summary": "This paper introduce GOT-R1 a framework apply reinforcement learning to enhance semantic special reasoning in autoregressive visual generation models. This method is build based on the generation chain-of-thought framework, which first generated the reasoning process given the prompt, and then generates images following the reasoning process. The motivation of incorporating reinforcement learning into the reasoning change generation is that the author observe that GoT-generated reasoning chains can be unfaithful to the prom, despite following templates well. Thus authors introduce GoT-R1, adapting reinforcement, learning, advances to enhance semantic spatial reasoning in autoregressive vision generation. Specifically, in GoT-R1, there is a comprehensive reward framework to ensure that effective of training, including rewards for prompt to reasoning semantic alignment, prompt-reasoning spatial alignment, reasoning–to–image alignment, and prompt-to-image alignment. Experiment results demonstrate that GoT-R1 achieves state of the art on T2I-CompBench and Geneval.", "questions": "1. In line 322, it is said that the parameter of MLLM are updated through LoRA finetune. As I understand, the NLLM is used as the reward model which should be fixed. Why the MLLM should be finetuned? \n2. During the experiment, Qwen2.5VL-7B is selected as the reward model. Have you evaluate how reliable Qwen2.5VL-7B is as the reward model?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper introduces an effective way to enhance the chain-of-thought reasoning capability of autoregressive visual generation model. Experiment results directly support the effectiveness of the proposed method. \n2. The reward framework is comprehensive, cross alignment between prompt, reasoning chain and images are considered. This rewards are general, and could provide inspiration to the rewards design of other visual generation model. \n3. The paper is well organised, clearly written and easy to follow and understand.", "weaknesses": "1. The experiments are conducted with Janus-Pro-1B and Janus-Pro-7B as the base model. It'll be interesting to see how the GoT-1 framework would perform with other base models. (I know it is not a necessary experiment.)\n2. The experiments really demonstrates that the GoT-R1 method help improve the quality of the final generated image. While the quality of chain of thought is not evaluated, especially compared to the original GoT. \n3. Some of reference of the table in the main text are wrong, for example,  table 4.4 in line 433, table 4.5 and line 445.", "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": "Y7Oq25NZJT", "reviewer": "Reviewer_y7UA", "summary": "This paper introduces ​​GoT-R1​​, a novel framework that enhances the ability of autoregressive visual generation models to handle complex, compositional text prompts. The key innovation is a ​​dual-stage, multi-dimensional reward function design for GRPO training​​ that uses a Multimodal LLM (MLLM) to evaluate both the reasoning processand the final image.", "questions": "- Why the total reward is defined as the product of individual rewards? Further exploration is warranted regarding why addition yields less than multiplication, ideally with more specific case studies on rewards.\n- How well does the reward model generalize to prompts that are out-of-domain or contain concepts not well-represented in the MLLM's training data?\n- Could you provide some failure cases for GoT-R1? I think analyzing a case where even the RL-trained model fails could reveal valuable insights into the remaining limitations of the approach, such as the fundamental constraints of the base autoregressive architecture or the reward design.\n- Can the rewards designed in this paper be used in other GRPO-like methods such DAPO [1]?\n\n\n[1] DAPO: An Open-Source LLM Reinforcement Learning System at Scale", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper leverages the proven success of RL (GRPO) in enhancing reasoning for language models like DeepSeek-R1and applies it to the challenging domain of compositional visual generation. The motivation is clear: predefined reasoning templates are a bottleneck, and RL is a natural solution for discovering better strategies.", "weaknesses": "- No code and data is provided to reproduce the experiments.\n- The approach is computationally expensive. Training involves sampling multiple candidates (N=16) per prompt, each evaluated by a 7B parameter MLLM (Qwen2.5-VL) as the reward model.\n- The paper directly applies the original GRPO algorithm with an improved reward function to the new model. As a technical contribution, the effort remains insufficient, with the primary novelty being several rewards.\n- The paper mentions T2I-R1 as concurrent work but does not provide a detailed comparison. Since T2I-R1 also uses RL for CoT in image generation, a clearer distinction of GoT-R1's unique contributions.", "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": "1X82MXoUbF", "reviewer": "Reviewer_bbst", "summary": "This paper proposes GoT-R1, an RL framework to improve autoregressive image generation models on complex text prompts by incorporating explicit reasoning. The approach introduces a dual-stage, multi-dimensional reward scheme: one stage evaluates the reasoning process itself, and another evaluates the final image, using vlm as judges. On multiple benchmarks, GoT-R1 shows significant improvements in placing objects correctly and binding attributes, outperforming the previous sota in complex prompt fidelity.", "questions": "For equation (3), the total reward is a sum (R_sem + R_spa) which is [0,2] times a bunch of other [0,1]s, wouldn't this make the total reward be [0,2]?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "* The method shows significant improvement on compositionally of image gen tasks.\n* Clear ablation study that supports the dual stage and reward design, RPI‑only helps but full reward performs best, while RRI‑only is harmful", "weaknesses": "* The idea of injecting chain-of-thought into image generation and refining with RL is not unique to this paper. For example: ReasonGen-R1, T2I-R1 are both very similar works and comparative discussions are limited. \n* The experiments seem mostly focused on the compositional benchmark, evaluation on broader text-to-image tasks are very limited.", "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)