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arxiv: 2508.20751 · v2 · submitted 2025-08-28 · 💻 cs.CV

Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning

Yibin Wang , Zhimin Li , Yuhang Zang , Yujie Zhou , Jiazi Bu , Chunyu Wang , Qinglin Lu , Cheng Jin
show 1 more author
Jiaqi Wang
This is my paper

Pith reviewed 2026-05-18 20:27 UTC · model grok-4.3

classification 💻 cs.CV
keywords Pref-GRPOtext-to-image generationreinforcement learningreward hackingpairwise preferenceGRPOUniGenBench
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The pith

By using win rates from pairwise comparisons, Pref-GRPO stabilizes reinforcement learning for text-to-image generation and reduces reward hacking.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors aim to solve the problem of reward hacking and training instability in GRPO methods for text-to-image generation. They show that pointwise reward models lead to illusory advantages when small score differences are normalized and amplified. Pref-GRPO addresses this by performing pairwise comparisons of images within each group and using the win rate as the reward signal instead. This changes the goal to fitting preferences rather than maximizing scores. They support this with experiments and introduce UniGenBench, a detailed benchmark using MLLM for evaluating semantic consistency in generated images.

Core claim

The central claim is that shifting the optimization from score maximization to preference fitting via win rates obtained from pairwise comparisons within groups using preference reward models yields more stable advantages and mitigates reward hacking in text-to-image reinforcement learning.

What carries the argument

Pairwise preference reward-based GRPO, in which the win rate from intra-group pairwise comparisons serves as the reward signal to replace normalized pointwise scores.

If this is right

  • Training becomes more stable by avoiding the amplification of minimal score differences.
  • The method better captures subtle differences in image quality.
  • Reward hacking is reduced in GRPO-based approaches for T2I.
  • UniGenBench enables more comprehensive evaluation of T2I models across multiple themes and criteria.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar pairwise preference approaches could improve stability in other areas of reinforcement learning from feedback.
  • The new benchmark may highlight specific weaknesses in current T2I models that pointwise evaluations miss.
  • Integrating preference fitting with other optimization techniques could lead to even better image generation results.

Load-bearing premise

That the preference reward models give accurate signals for subtle quality differences and that the main cause of prior instability is the normalization amplifying tiny score gaps.

What would settle it

A demonstration that Pref-GRPO exhibits reward hacking or instability when applied to certain preference models, or that the win rates fail to predict human preferences on image pairs.

Figures

Figures reproduced from arXiv: 2508.20751 by Cheng Jin, Chunyu Wang, Jiaqi Wang, Jiazi Bu, Qinglin Lu, Yibin Wang, Yuhang Zang, Yujie Zhou, Zhimin Li.

Figure 1
Figure 1. Figure 1: Method Overview. (a) Existing pointwise reward functions assign minimal score differ￾ences between generated images, which result in illusory advantage and ultimately lead to reward hacking. (b) PREF-GRPO shifts the training focus from reward score maximization to pairwise preference fitting, enabling stable optimization for T2I generation. ABSTRACT Recent advancements underscore the significant role of GR… view at source ↗
Figure 2
Figure 2. Figure 2: Reward Hacking Visualization. the small gaps are disproportionately amplified. Un￾der a reward-maximization objective, such inflated advantages drive the policy to over-optimize for triv￾ial reward cues, and this sustained pressure ultimately steers it toward reward-hacking behaviors that rapidly increase scores but destabilize the generation process (examples shown in Figs. 1 and 2). Besides, if the re￾wa… view at source ↗
Figure 3
Figure 3. Figure 3: Benchmark Statistics and Evaluation Results. This figure presents (a) prompt themes, (b) subject distribution, and evaluation dimensions (testpoints) of UNIGENBENCH, along with benchmarking results for both open-source and closed-source T2I models. Style World Know. Attribute Quant. Expn. Material Size Shape Color Action Hand Full Body Animal Non Contact --- --- Contact State Relationship Compo. Similarity… view at source ↗
Figure 4
Figure 4. Figure 4: Benchmark Comparison. While current methods only support scoring at the primary dimensions, our benchmark provides fine-grained evaluation across both primary and sub dimensions. (1) PREF-GRPO incorporates a pairwise preference RM (PPRM) (Wang et al., 2025a), reformulating the GRPO optimization objective from conventional absolute reward score maximization to pairwise preference fitting. As illustrated in … view at source ↗
Figure 5
Figure 5. Figure 5: UNIGENBENCH Construction and Evaluation Pipeline. We leverage powerful MLLM for (a) large-scale and diverse prompts generation, and (b) scalable and fine-grained T2I evaluation. 4.2 BENCHMARK CONSTRUCTION AND EVALUATION PIPELINE Having established diverse prompt themes, subject categories, and evaluation dimensions, we further construct an MLLM-based automated pipeline to operationalize the benchmark shown… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative Comparison. We compare PREF-GRPO with several pointwise RM-based GRPO methods, demonstrating its superior performance and effectiveness [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative Results of UnifiedReward Score-based Winrate as Reward: We convert UnifiedReward scores into win rates as rewards for GRPO, and observe that this effectively mitigates the reward hacking issue. A.2 POINT SCORE-BASED WINRATE V.S. PAIRWISE PREFERENCE-BASED WINRATE To demonstrate that shifting the training objective to our proposed pairwise preference fitting enhances stability and that pairwise c… view at source ↗
Figure 8
Figure 8. Figure 8: Reward Hacking Visualization of HPS. At around step 160, the image quality begins to degrade, even though the reward score continues to rise, indicating the occurrence of reward hacking. A.4 MORE REWARD HACKING ANALYSIS We further visualize the phenomenon of reward hacking when using HPS (Wu et al., 2023) as the pointwise reward model. As shown in [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative Results of Joint Optimization. Joint training with CLIP improves semantic consistency while slightly degrading perceptual quality, reflecting the inherent trade-off. A beige pastry sitting in a white ball next to a spoon. A dragon-shaped teapot. A desert scene with sand that transitions from gold to sapphire blue. A surfboard shaped like a lightning bolt, balanced on a volcanic rock. Tsaurus re… view at source ↗
Figure 10
Figure 10. Figure 10: More Qualitative Comparison. We compare PREF-GRPO with several pointwise RM-based GRPO methods, demonstrating its superior performance and effectiveness. 2024), Bagel (Deng et al., 2025), BLIP3-o (Chen et al., 2025a), CogVideo4 (Ding et al., 2021), Hunyuan-DiT (Li et al., 2024b), Janus (Wu et al., 2025b), Janus-flow (Ma et al., 2024), Emu3 (Wang et al., 2024), Playground2.5 (Li et al., 2024a), and SDXL (R… view at source ↗
Figure 11
Figure 11. Figure 11: Fine-grained Benchmarking Results of T2I models on UNIGENBENCH. Best scores are in green, second-best in yellow. A row of penguins stood neatly on the ice sheet. They all held their heads high and looked at the aurora... In an (Impressionist/surrealist...) oil painting... An Art Deco sculpture... Dark fantasy art style, an ancient magic book with several exquisite brass hands sticking out from the cover a… view at source ↗
Figure 12
Figure 12. Figure 12: Prompt Themes of UNIGENBENCH. We provide representative prompt examples for each theme to facilitate understanding. B.4 SUBJECT CATEGORIES As shown in [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Evaluation Dimensions of UNIGENBENCH. We provide representative prompt examples for each evaluation dimension to facilitate understanding. Distribution of the number of testpoints per prompt 64 155 159 147 75 Number of Testpoints Number of Prompts 1 2 3 4 5 [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Distribution of Testpoint Counts in Prompts. This figure presents the distribution of the number of testpoints per prompt in UNIGENBENCH. • Facial Expressions: Assesses whether generated characters exhibit correct and contextually appropriate emotions. • Pronoun Reference: Tests the model’s capability to resolve ambiguous pronouns (e.g., its, him) correctly. • Hand Actions: Examines whether fine-grained h… view at source ↗
read the original abstract

Recent advancements highlight the importance of GRPO-based reinforcement learning methods and benchmarking in enhancing text-to-image (T2I) generation. However, current methods using pointwise reward models (RM) for scoring generated images are susceptible to reward hacking. We reveal that this happens when minimal score differences between images are amplified after normalization, creating illusory advantages that drive the model to over-optimize for trivial gains, ultimately destabilizing the image generation process. To address this, we propose Pref-GRPO, a pairwise preference reward-based GRPO method that shifts the optimization objective from score maximization to preference fitting, ensuring more stable training. In Pref-GRPO, images are pairwise compared within each group using preference RM, and the win rate is used as the reward signal. Extensive experiments demonstrate that PREF-GRPO differentiates subtle image quality differences, providing more stable advantages and mitigating reward hacking. Additionally, existing T2I benchmarks are limited by coarse evaluation criteria, hindering comprehensive model assessment. To solve this, we introduce UniGenBench, a unified T2I benchmark comprising 600 prompts across 5 main themes and 20 subthemes. It evaluates semantic consistency through 10 primary and 27 sub-criteria, leveraging MLLM for benchmark construction and evaluation. Our benchmarks uncover the strengths and weaknesses of both open and closed-source T2I models and validate the effectiveness of Pref-GRPO.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The paper claims that pointwise reward model scores in GRPO for text-to-image generation lead to reward hacking because normalization amplifies minimal differences into unstable advantages. Pref-GRPO addresses this by using a preference reward model to perform pairwise comparisons within groups of images and deriving win rates as the reward signal, shifting the objective to preference fitting. The work also introduces UniGenBench, a benchmark with 600 prompts across 5 themes and 20 subthemes, using MLLM-based evaluation on 10 primary and 27 sub-criteria for semantic consistency. Experiments are said to show more stable training and better differentiation of subtle quality differences.

Significance. If the central empirical claims hold, Pref-GRPO would represent a targeted improvement to GRPO-style RL for T2I models by replacing a known source of advantage instability with pairwise preference signals. UniGenBench could fill a gap in coarse existing T2I evaluation by providing finer-grained, multi-criteria assessment. The approach builds directly on cited GRPO and preference RM components without introducing new free parameters in the core formulation.

major comments (3)
  1. [§3] §3 (Method): The claim that pairwise win rates from the preference RM yield stable advantages rests on the unverified assumption that the RM supplies unbiased signals for subtle differences; no inter-rater agreement, cycle detection, or adversarial robustness results for the RM are reported, which is load-bearing for the reward-hacking mitigation argument.
  2. [§4.2] §4.2 (Experiments): The reported stability improvements lack controls or ablations isolating the effect of win-rate normalization versus other factors such as policy gradient variance or prompt distribution; without these, attribution of gains to the pairwise formulation is not fully secured.
  3. [Table 3] Table 3 or equivalent results table: No standard deviations, number of random seeds, or statistical significance tests are provided for the metric gains over GRPO baselines, weakening the cross-method stability claim.
minor comments (3)
  1. [Abstract] Abstract: Inconsistent capitalization of 'Pref-GRPO' versus 'PREF-GRPO' should be unified.
  2. [§2] §2 (Related Work): GRPO acronym is used before its first expansion; add the full name on first occurrence.
  3. [Figure 2] Figure 2: The pipeline diagram would be clearer with explicit annotation of the within-group pairwise comparison step and win-rate aggregation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below, acknowledging where the original manuscript could be strengthened and outlining planned revisions.

read point-by-point responses

  1. Referee: [§3] §3 (Method): The claim that pairwise win rates from the preference RM yield stable advantages rests on the unverified assumption that the RM supplies unbiased signals for subtle differences; no inter-rater agreement, cycle detection, or adversarial robustness results for the RM are reported, which is load-bearing for the reward-hacking mitigation argument.

    Authors: We agree that the reliability of the preference RM is central to the argument. The RM employed is a standard model pretrained on large-scale human preference datasets for T2I tasks, as cited in the manuscript. While the original submission did not report inter-rater agreement, cycle detection, or adversarial robustness tests specific to this RM, the pairwise formulation mitigates sensitivity to small absolute score variations by design. To address the concern, we will add a dedicated paragraph in §3 discussing the RM's established properties from prior literature and explicitly noting the absence of custom robustness experiments as a limitation. revision: yes

  2. Referee: [§4.2] §4.2 (Experiments): The reported stability improvements lack controls or ablations isolating the effect of win-rate normalization versus other factors such as policy gradient variance or prompt distribution; without these, attribution of gains to the pairwise formulation is not fully secured.

    Authors: The referee correctly identifies that stronger isolation of the win-rate component would improve attribution. Our experiments primarily compared full Pref-GRPO against GRPO baselines and showed improved stability, but did not include targeted ablations holding gradient variance and prompt distribution fixed while varying only the reward signal. We will incorporate additional ablation experiments in the revised §4.2 that systematically vary the reward formulation (pointwise vs. pairwise win-rate) under controlled conditions to better isolate its contribution. revision: yes

  3. Referee: [Table 3] Table 3 or equivalent results table: No standard deviations, number of random seeds, or statistical significance tests are provided for the metric gains over GRPO baselines, weakening the cross-method stability claim.

    Authors: We acknowledge that the lack of variability measures and statistical tests limits the strength of the stability claims. The original tables reported mean performance metrics without standard deviations, seed counts, or significance testing. In the revision we will update Table 3 and related result tables to include standard deviations computed over multiple random seeds (at least three) and add statistical significance indicators for key comparisons against the GRPO baseline. revision: yes

Circularity Check

0 steps flagged

No significant circularity: Pref-GRPO introduces independent pairwise reformulation on top of cited GRPO and preference RM components

full rationale

The paper's central move replaces pointwise RM scoring and normalization with within-group pairwise win rates from a preference RM as the reward signal. This is presented as a direct methodological substitution rather than a derivation that reduces by construction to fitted parameters, self-definitions, or prior self-citations. No equations or load-bearing steps in the provided abstract or description equate the output advantage stability to the input normalization step or to any self-referential loop. The claim of mitigating reward hacking rests on the new objective having independent content, consistent with the reader's assessment of score 2.0 for minor non-load-bearing elements only.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Relies on standard GRPO optimization and existing preference reward models from prior work; no new free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption Preference reward models can reliably distinguish subtle quality differences between images in a manner superior to pointwise scoring after normalization.
    Invoked to justify replacing pointwise scores with win rates as the reward signal.

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