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arxiv: 2605.28203 · v1 · pith:353ECJWKnew · submitted 2026-05-27 · 💻 cs.LG

Refining Multidimensional Video Reward Models via Disentangled Influence Functions

Muyao Wang , Zeke Xie , Hideki Nakayama This is my paper

Pith reviewed 2026-06-29 13:43 UTC · model grok-4.3

classification 💻 cs.LG
keywords multidimensional video reward modelstext-to-video generationinfluence functionsdata pruningreweightingdimensional heterogeneitysupervision risk
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The pith

Disentangled influence functions estimate per-dimension supervision risk to refine multidimensional video reward models beyond global filtering.

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

The paper identifies that training samples for video reward models have reliability that varies across different evaluation dimensions, a phenomenon called dimensional heterogeneity. Global filtering methods that use a single metric for all dimensions are therefore ill-suited for these tasks. The authors propose a disentangled influence framework to estimate risk separately for each dimension. Using this, they develop pruning and reweighting strategies that remove or downweight high-risk samples on a per-dimension basis. Experiments show these strategies produce reward models more aligned with ground truth than standard approaches.

Core claim

The central claim is that addressing dimensional heterogeneity through a disentangled influence framework enables dimension-specific data refinement strategies, specifically Dimension-Disentangled Pruning and Dimension-Disentangled Reweighting, which outperform global filtering baselines in aligning multidimensional video reward models with ground truth.

What carries the argument

The disentangled influence framework, which efficiently estimates dimension-specific supervision risk for each evaluation dimension.

If this is right

  • Dimension-Disentangled Pruning removes samples with extreme high-risk for specific dimensions.
  • Dimension-Disentangled Reweighting softly down-weights high-risk supervision per dimension.
  • These yield reward models with superior alignment to ground truth compared to global methods.
  • The approach handles the complex nature of video data in T2V tasks more effectively than scalar-metric filters.

Where Pith is reading between the lines

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

  • The framework could extend to other multimodal tasks where evaluation dimensions show varying data reliability.
  • Influence functions may be adapted more broadly for fine-grained curation when scalar metrics fall short.
  • Scalability tests on larger video datasets could expose limits in the risk estimation process.

Load-bearing premise

Dimension-specific supervision risk can be reliably estimated by the disentangled influence framework without the estimation itself introducing new biases or requiring unavailable per-dimension ground-truth labels.

What would settle it

An experiment on a held-out test set where the disentangled pruning and reweighting strategies produce no better or worse alignment metrics than global filtering baselines would falsify the performance claim.

Figures

Figures reproduced from arXiv: 2605.28203 by Hideki Nakayama, Muyao Wang, Zeke Xie.

Figure 1
Figure 1. Figure 1: Dimensional Heterogeneity in Video Training Data. We visualize the distribution of sample self-influence scores for Visual Quality (x￾axis) versus Dynamic Degree (y-axis). The scatter plot exhibits a dispersed distribution with negli￾gible correlation, indicating that supervision reli￾ability is dimension-specific. Samples with high self-influence in one dimension, which we use as a proxy for dimension-spe… view at source ↗
Figure 2
Figure 2. Figure 2: We analyze the impact of removing the top- [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Dimensional Heterogeneity across reward dimensions. We visualize the relationship between Visual Quality influence (x-axis) and three other reward dimensions (y-axis): (a) Temporal Consistency, (b) Text Alignment, and (c) Factual Consistency. The color indicates the normalized Standard Influence (Total Loss). The distinct “L-shaped” distribution reveals that supervision reliability is disentangled. Crucial… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative Visualization of Label Noise in T2V Alignment. We show samples with high T2V self-influence but low visual self-influence. Although they are labeled as Score 1 (Worst Alignment) in the training set, human re-evaluation rates them as Score 4, as the videos match substantial parts of their prompts, such as “a knight on horse” or “kid sleeping with cat”. This confirms label noise in the T2V alignm… view at source ↗
Figure 5
Figure 5. Figure 5: Scatter plot comparisons across dimensions. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Label noise visualization. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Label noise visualization. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
read the original abstract

As Text-to-Video (T2V) generation models continue to evolve, the complexity of video evaluation necessitates a fine-grained assessment across various axes. To address this, recent works have focused on developing Multidimensional Video Reward Models (MVRMs), which decompose the evaluation process to better align with the multifaceted nature of human visual perception. However, training effective MVRMs is fundamentally challenged by the complex nature of video data. In this work, we identify a critical phenomenon termed Dimensional Heterogeneity: the reliability of a training sample can vary substantially across evaluation dimensions, meaning that a sample may provide reliable supervision for one objective while inducing high supervision risk for another. Consequently, prevailing data-centric methods that filter based on global scalar metrics are ill-posed for T2V tasks. To address this, we propose a disentangled influence framework that that efficiently estimates dimension-specific supervision risk. Leveraging this framework, we introduce two dimension-disentangled refinement strategies: Dimension-Disentangled Pruning, which removes extreme high-risk samples, and Dimension-Disentangled Reweighting, which softly down-weights high-risk supervision. Extensive experiments demonstrate that our disentangled strategies significantly outperform global filtering baselines, yielding reward models with superior alignment to ground truth.

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 identifies a phenomenon called Dimensional Heterogeneity in training data for Multidimensional Video Reward Models (MVRMs) for Text-to-Video generation, where a sample's reliability can differ substantially across evaluation dimensions. It argues that global scalar filtering is therefore ill-posed and proposes a disentangled influence framework to estimate per-dimension supervision risk. From this it derives two strategies—Dimension-Disentangled Pruning (removing extreme high-risk samples) and Dimension-Disentangled Reweighting (softly down-weighting high-risk supervision)—and reports that both outperform global filtering baselines, producing reward models with superior alignment to ground truth.

Significance. If the dimension-specific influence estimates prove accurate and the reported gains hold under proper controls, the work would offer a practical data-centric improvement for training fine-grained reward models on complex video data, where scalar metrics are known to be insufficient.

major comments (2)
  1. [Abstract (and § on disentangled influence framework)] The central claim that the disentangled strategies outperform global baselines rests on the accuracy of the per-dimension supervision-risk estimates. The abstract states that per-dimension ground-truth labels are unavailable in practice, yet provides no alternative validation (e.g., synthetic probes, human correlation studies, or ablation on known noisy dimensions) that the influence-derived scores recover true dimension-specific quality rather than shared video-feature artifacts. Without such evidence the reported gains could be artifacts of the estimation procedure itself.
  2. [Disentangled influence framework description] Influence functions are defined on a scalar loss; the manuscript must specify the exact decomposition (loss splitting, gradient attribution, or auxiliary heads) used to obtain dimension-specific scores. If the decomposition correlates estimates across dimensions or re-introduces the global metric through shared parameters, the “disentangled” claim is undermined. No equation or algorithmic box in the provided description clarifies this step.
minor comments (1)
  1. [Abstract] Abstract contains a repeated word: “framework that that efficiently”.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to improve clarity and evidence.

read point-by-point responses

  1. Referee: [Abstract (and § on disentangled influence framework)] The central claim that the disentangled strategies outperform global baselines rests on the accuracy of the per-dimension supervision-risk estimates. The abstract states that per-dimension ground-truth labels are unavailable in practice, yet provides no alternative validation (e.g., synthetic probes, human correlation studies, or ablation on known noisy dimensions) that the influence-derived scores recover true dimension-specific quality rather than shared video-feature artifacts. Without such evidence the reported gains could be artifacts of the estimation procedure itself.

    Authors: We agree that the absence of direct per-dimension ground-truth labels makes validation important. The reported gains in ground-truth alignment provide indirect support that the estimates capture dimension-specific effects rather than artifacts, as global baselines underperform. To strengthen this, we will add synthetic probe experiments with controlled dimension-specific noise injection in the revision. revision: yes

  2. Referee: [Disentangled influence framework description] Influence functions are defined on a scalar loss; the manuscript must specify the exact decomposition (loss splitting, gradient attribution, or auxiliary heads) used to obtain dimension-specific scores. If the decomposition correlates estimates across dimensions or re-introduces the global metric through shared parameters, the “disentangled” claim is undermined. No equation or algorithmic box in the provided description clarifies this step.

    Authors: The full manuscript specifies the decomposition via auxiliary dimension-specific heads combined with a per-dimension loss split in the framework section. We will add an explicit algorithmic box and equations in the revision to clarify the procedure and confirm that shared parameters do not reintroduce global metrics. revision: yes

Circularity Check

0 steps flagged

No circularity: framework and empirical claims are independent of inputs

full rationale

The provided abstract and description introduce Dimensional Heterogeneity as an observed phenomenon and propose a disentangled influence framework to estimate per-dimension supervision risk, followed by pruning and reweighting strategies. No equations, derivations, or self-citations are present that reduce any prediction or uniqueness claim to a fitted parameter or prior author result by construction. The central claim of outperformance over global baselines is presented as an empirical finding from experiments, not a definitional or self-referential necessity. This is the most common honest outcome when no load-bearing reduction is exhibited.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no concrete free parameters, axioms, or invented entities can be identified from the manuscript.

pith-pipeline@v0.9.1-grok · 5748 in / 1121 out tokens · 33154 ms · 2026-06-29T13:43:14.733386+00:00 · methodology

discussion (0)

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