Visual Autoregressive Models Beat Diffusion Models on Inference Time
  Scaling
Authors: name: Erik Riise, name: Mehmet Onurcan Kaya, name: Dim P. Papadopoulos

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.