arXiv:2512.10794v1 Announce Type: cross 
Abstract: Representation alignment (REPA) guides generative training by distilling representations from a strong, pretrained vision encoder to intermediate diffusion features. We investigate a fundamental question: what aspect of the target representation matters for generation, its \textit{global} \revision{semantic} information (e.g., measured by ImageNet-1K accuracy) or its spatial structure (i.e. pairwise cosine similarity between patch tokens)? Prevalent wisdom holds that stronger global semantic performance leads to better generation as a target representation. To study this, we first perform a large-scale empirical analysis across 27 different vision encoders and different model scales. The results are surprising; spatial structure, rather than global performance, drives the generation performance of a target representation. To further study this, we introduce two straightforward modifications, which specifically accentuate the transfer of \emph{spatial} information. We replace the standard MLP projection layer in REPA with a simple convolution layer and introduce a spatial normalization layer for the external representation. Surprisingly, our simple method (implemented in $<$4 lines of code), termed iREPA, consistently improves convergence speed of REPA, across a diverse set of vision encoders, model sizes, and training variants (such as REPA, REPA-E, Meanflow, JiT etc). %, etc. Our work motivates revisiting the fundamental working mechanism of representational alignment and how it can be leveraged for improved training of generative models. The code and project page are available at https://end2end-diffusion.github.io/irepa

دراسة حديثة نُشرت على arXiv تبحث في العوامل التي تؤثر على محاذاة التمثيل في التدريب التوليدي، حيث تركز على أهمية المعلومات الدلالية العالمية مقارنةً بالهيكل المكاني لأداء التوليد. تكشف النتائج أن الهيكل المكاني، بدلاً من الأداء العالمي، له تأثير كبير على فعالية التمثيلات المستهدفة في النماذج التوليدية.

Un estudio reciente publicado en arXiv investiga los factores que influyen en la alineación de representaciones en el entrenamiento generativo, examinando específicamente si la información semántica global o la estructura espacial es más crítica para el rendimiento de generación. Los hallazgos revelan que la estructura espacial, en lugar del rendimiento global, impacta significativamente en la efectividad de las representaciones objetivo en los modelos generativos.

Une étude récente publiée sur arXiv examine les facteurs influençant l'alignement de représentation dans l'entraînement génératif, en se concentrant sur l'importance de l'information sémantique globale par rapport à la structure spatiale pour la performance de génération. Les résultats révèlent que la structure spatiale, plutôt que la performance globale, a un impact significatif sur l'efficacité des représentations cibles dans les modèles génératifs.

A recent study published on arXiv investigates the factors influencing representation alignment in generative training, specifically examining whether global semantic information or spatial structure is more critical for generation performance. The findings reveal that spatial structure, rather than global performance, significantly impacts the effectiveness of target representations in generative models.

What matters for Representation Alignment: Global Information or Spatial Structure?

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