Visual Autoregressive Models Beat Diffusion Models on Inference Time Scaling

Erik Riise, onurcan, Dim P. Papadopoulos

Visual Autoregressive Models Beat Diffusion Models on Inference Time Scaling: 21 upvotes on Hugging Face Daily Papers, #12 of 36 papers on 2025-10-21. Day-by-day upvote history.

While inference-time scaling through search has revolutionized Large Language Models, translating these gains to image generation has proven difficult. Recent attempts to apply search strategies to continuous diffusion models show limited benefits, with simple random sampling often performing best. We demonstrate that the discrete, sequential nature of visual autoregressive models enables effective search for image generation. We show that beam search substantially improves text-to-image generation, enabling a 2B parameter autoregressive model to outperform a 12B parameter diffusion model across benchmarks. Systematic ablations show that this advantage comes from the discrete token space, which allows early pruning and computational reuse, and our verifier analysis highlights trade-offs between speed and reasoning capability. These findings suggest that model architecture, not just scale, is critical for inference-time optimization in visual generation.

Paper page on Hugging Face · arXiv

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