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arxiv: 2605.25661 · v2 · pith:WDCU245Anew · submitted 2026-05-25 · 💻 cs.CV

DRM: Diffusion-based Reward Model With Step-wise Guidance

Jaxon Zhang , Binxin Yang , Hubery Yin , Chen Li , Jing Lyu This is my paper

Pith reviewed 2026-06-29 22:10 UTC · model grok-4.3

classification 💻 cs.CV
keywords diffusion modelreward modelpreference alignmentimage generationreinforcement learningstep-wise guidanceGRPO
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The pith

A pre-trained diffusion model can serve as a reward model that scores both final images and noisy intermediate latents to align generation with human preferences.

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

The paper claims that because diffusion models already generate high-fidelity images, they must encode a deeper grasp of aesthetics, composition, and harmony than vision-language models trained only for semantic matching. It therefore treats the diffusion model itself as the reward backbone, allowing evaluation at every denoising step rather than only at the end. This step-wise capacity is used first to supply dense per-step rewards inside a modified GRPO reinforcement-learning procedure and second to score multiple candidate paths at each inference step. Experiments show the resulting images have measurably higher perceptual quality than those aligned with conventional VLM reward models.

Core claim

The Diffusion-based Reward Model (DRM) turns a pre-trained diffusion model into an evaluative backbone whose denoising trajectory supplies reward signals on both clean and noisy latents, enabling Step-wise GRPO to assign credit at every timestep and Step-wise Sampling to prune inferior trajectories on the fly.

What carries the argument

The Diffusion-based Reward Model (DRM), which re-uses the pre-trained diffusion model's noise-prediction network to score perceptual quality at arbitrary points along the generative trajectory.

If this is right

  • Step-wise GRPO replaces sparse terminal rewards with per-denoising-step signals, reducing credit-assignment error in reinforcement-learning alignment.
  • Step-wise Sampling lets the DRM choose among multiple latent paths at each timestep, steering the trajectory toward higher-quality endpoints.
  • The method eliminates the need for a separate VLM reward model trained on semantic tasks.
  • Final generated images exhibit improved perceptual quality across standard human-preference benchmarks.

Where Pith is reading between the lines

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

  • The same diffusion backbone could supply reward signals for other iterative generative processes such as video or 3-D synthesis.
  • Training separate reward models might become unnecessary if the generator itself already encodes the relevant perceptual criteria.
  • The approach invites direct comparison between diffusion-based and flow-matching-based reward signals on identical tasks.

Load-bearing premise

A model that can generate high-fidelity images must already possess a deep internal understanding of aesthetics, composition, and visual harmony.

What would settle it

A controlled test in which the same diffusion model, when used as DRM, assigns higher scores to images that human raters judge lower in aesthetics or composition than to images the raters judge higher.

Figures

Figures reproduced from arXiv: 2605.25661 by Binxin Yang, Chen Li, Hubery Yin, Jaxon Zhang, Jing Lyu.

Figure 1
Figure 1. Figure 1: Comparison between preview reward models and DRM. Existing reward models treat the generation process as a black box, providing only a single, terminal reward based on the final output. Our DRM offers fine-grained reward for any noisy latent along the entire denoising trajectory. 0 100 200 300 400 500 600 Step 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Diffusion-based Reward Model Step-GRPO GRPO 2.5xEfficiency [PITH… view at source ↗
Figure 2
Figure 2. Figure 2: Reward curves for various RL algorithms optimized using our DRM. Our Step-GRPO, which leverages dense, per￾step rewards, not only reaches a higher final reward but also con￾verges 2.5x faster on step than the standard GRPO baseline. learned reward function can then be used to generate syn￾thetic preference data, significantly reducing the reliance on manual annotation for model alignment. Early RMs [19, 43… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the Diffusion-based Reward Model (DRM). (Left) The training pipeline. During training, the DRM takes a pair of preferred and dispreferred images, both corrupted with noise at a specific timestep t, and predicts their respective reward scores. The model is then optimized via DRM loss. (Right) The detailed architecture of our Reward Output Head. . . . . . . SDE Initilize K individual start point … view at source ↗
Figure 4
Figure 4. Figure 4: GRPO vs. Step-wise GRPO. (Left) Naive GRPO relies on a terminal reward. It samples multiple full trajectories, calculates a single reward at the final step (t=0), and applies this coarse reward uniformly to all preceding steps, leading to imprecise credit assignment. (Right) Step-wise GRPO introduces a dense, per-step reward signal. From a single initial point, it explores k candidate samples via SDE at ea… view at source ↗
Figure 5
Figure 5. Figure 5: Overview of Step-wise Sampling. At each step t, we perform a branching into k candidates via SDE. The DRM scores these candidates, and the top-scoring latent is chosen to continue the trajectory. a training-free, plug-and-play mechanism for improving model outputs. This approach provides a highly practi￾cal method for users to boost generation quality without any model fine-tuning. Conventional determinist… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison of SD3.5-Medium optimized by various reward models. Our approach clearly exhibits superior visual quality compared to the competing methods. Model ImageReward PickScore HPSv3 SD3.5-Medium 1.01 16.76 8.95 + PickScore & GRPO 1.14 16.94 9.64 + HPSv3 & GRPO 1.15 16.90 9.71 + DRM & GRPO 1.14 16.95 10.07 + DRM & Step-GRPO 1.17 17.04 10.28 [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Reward curves with steps and GPU hours as the x-axis. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Step-wise Sampling enhances both the fidelity to the [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
read the original abstract

Current mainstream methods of aligning diffusion models with human preferences typically employ VLM-based reward models. However, these reward models, pre-trained for semantic alignment, struggle to capture the essential perceptual qualities-such as aesthetics, composition, and visual harmony. In this work, we argue that a model capable of high-fidelity generation must possess a profound understanding of these visual attributes. Based on this insight, we introduce the Diffusion-based Reward Model (DRM), a novel paradigm that use the pre-trained diffusion model as a powerful evaluative backbone. A key advantage of the DRM is its unique ability to assess not only the final image but also the noisy intermediate latents at any stage of the generative process. We leverage this step-wise evaluative capacity in two ways. First, we propose Step-wise GRPO, a reinforcement learning algorithm that provides dense, per-step rewards to resolve the imprecise credit assignment problem in GRPO algorithm, leading to more stable and effective alignment. Second, we introduce Step-wise Sampling, a novel inference strategy that employs the DRM as a dynamic guide to evaluate multiple generation paths at each step, steering the process towards higher-quality outcomes. Extensive experiments confirm that our approach significantly enhances the final quality of generated images. Code: https://github.com/jjaxonx/DRM.

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

2 major / 1 minor

Summary. The paper introduces the Diffusion-based Reward Model (DRM), which repurposes a pre-trained diffusion model as a reward backbone capable of evaluating both final images and intermediate noisy latents. This step-wise capacity is used to define Step-wise GRPO (a dense-reward variant of GRPO) for RL-based alignment and Step-wise Sampling (a multi-path guidance strategy at inference). The central motivation is that diffusion models' high-fidelity generation implies they already encode the perceptual attributes (aesthetics, composition, visual harmony) needed for reward modeling, offering an alternative to VLM-based rewards.

Significance. If the core assumption holds and the diffusion backbone's representations can be shown to correlate with human perceptual judgments independently of the generation task, DRM would supply a new, architecture-native reward signal for diffusion alignment. The step-wise formulation could address credit assignment in RL and enable dynamic guidance during sampling, potentially improving stability and final image quality over current VLM-based pipelines.

major comments (2)
  1. [Abstract, paragraph 2] Abstract, paragraph 2: The load-bearing claim that 'a model capable of high-fidelity generation must possess a profound understanding of these visual attributes' is asserted without any supporting analysis, correlation study, or ablation. No evidence is given that the denoising objective produces features that reliably distinguish or score aesthetics/composition/visual harmony beyond what is already required for generation. This directly underpins both the superiority claim versus VLM rewards and the utility of Step-wise GRPO and Step-wise Sampling.
  2. [Abstract] Abstract: The manuscript states that 'Extensive experiments confirm that our approach significantly enhances the final quality of generated images,' yet provides no quantitative results, baselines, ablations, or human-study details. Without these, it is impossible to assess whether the reported gains are attributable to the DRM formulation or to other factors.
minor comments (1)
  1. [Abstract] Abstract: grammatical error ('that use the pre-trained' should be 'that uses the pre-trained').

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the abstract. We address each point below and will revise the abstract accordingly to better support our claims with evidence from the full manuscript.

read point-by-point responses

  1. Referee: [Abstract, paragraph 2] Abstract, paragraph 2: The load-bearing claim that 'a model capable of high-fidelity generation must possess a profound understanding of these visual attributes' is asserted without any supporting analysis, correlation study, or ablation. No evidence is given that the denoising objective produces features that reliably distinguish or score aesthetics/composition/visual harmony beyond what is already required for generation. This directly underpins both the superiority claim versus VLM rewards and the utility of Step-wise GRPO and Step-wise Sampling.

    Authors: We agree the abstract presents the claim without immediate supporting data. The full manuscript includes ablations, correlation analyses with perceptual metrics, and human preference studies showing that diffusion features from the denoising process align with aesthetics and composition judgments beyond generation requirements alone. We will revise the abstract to include a brief reference to these results. revision: yes

  2. Referee: [Abstract] Abstract: The manuscript states that 'Extensive experiments confirm that our approach significantly enhances the final quality of generated images,' yet provides no quantitative results, baselines, ablations, or human-study details. Without these, it is impossible to assess whether the reported gains are attributable to the DRM formulation or to other factors.

    Authors: We acknowledge that the abstract summarizes the outcome without specific metrics or protocol details. The full manuscript reports quantitative gains against baselines, ablations isolating the DRM contribution, and human studies. We will revise the abstract to incorporate key quantitative results and evaluation details. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper presents DRM as a new paradigm grounded in an explicit argumentative insight (high-fidelity generation implies perceptual understanding), followed by two algorithmic proposals (Step-wise GRPO and Step-wise Sampling). No equations, fitted parameters, or predictions are shown to reduce by construction to inputs. No self-citations appear as load-bearing justifications for uniqueness or ansatzes. The derivation chain is self-contained and does not match any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated beyond the domain assumption that diffusion models encode perceptual qualities.

axioms (1)
  • domain assumption A model capable of high-fidelity generation must possess a profound understanding of visual attributes such as aesthetics, composition, and visual harmony.
    Invoked in abstract paragraph 2 as the justification for using the diffusion model as reward backbone.

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discussion (0)

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    watercolour beaver with tale, white background Model Overall↑ Single Obj.↑Two Obj.↑Counting↑Colors↑Position↑Attr. Binding↑ Flow Matching Models FLUX.1 Dev 0.66 0.98 0.81 0.74 0.79 0.22 0.45 SD3.5-L 0.71 0.98 0.89 0.73 0.83 0.34 0.47 SD3.5-M 0.63 0.98 0.78 0.50 0.81 0.24 0.52 GRPO based Methods SD3.5-M+Step-GRPO 0.78 0.99 0.93 0.80 0.86 0.37 0.70 Table S1....