Inference-Time Scaling for Visual AutoRegressive modeling by Searching Representative Samples

• URL: http://arxiv.org/abs/2601.07293v1
• Date: Mon, 12 Jan 2026 08:00:55 GMT
• Title: Inference-Time Scaling for Visual AutoRegressive modeling by Searching Representative Samples
• Authors: Weidong Tang, Xinyan Wan, Siyu Li, Xiumei Wang,
• Abstract summary: We introduce VAR-Scaling, the first general framework for inference-time scaling in visual autoregressive modeling (VQ)<n>We map sampling spaces to quasi-continuous feature spaces via kernel density estimation (KDE), where high-density samples approximate stable, high-quality solutions.<n> Experiments in class-conditional and text-to-image evaluations demonstrate significant improvements in inference process.
• Score: 8.364449021192016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While inference-time scaling has significantly enhanced generative quality in large language and diffusion models, its application to vector-quantized (VQ) visual autoregressive modeling (VAR) remains unexplored. We introduce VAR-Scaling, the first general framework for inference-time scaling in VAR, addressing the critical challenge of discrete latent spaces that prohibit continuous path search. We find that VAR scales exhibit two distinct pattern types: general patterns and specific patterns, where later-stage specific patterns conditionally optimize early-stage general patterns. To overcome the discrete latent space barrier in VQ models, we map sampling spaces to quasi-continuous feature spaces via kernel density estimation (KDE), where high-density samples approximate stable, high-quality solutions. This transformation enables effective navigation of sampling distributions. We propose a density-adaptive hybrid sampling strategy: Top-k sampling focuses on high-density regions to preserve quality near distribution modes, while Random-k sampling explores low-density areas to maintain diversity and prevent premature convergence. Consequently, VAR-Scaling optimizes sample fidelity at critical scales to enhance output quality. Experiments in class-conditional and text-to-image evaluations demonstrate significant improvements in inference process. The code is available at https://github.com/WD7ang/VAR-Scaling.

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