Visual Autoregressive Models Beat Diffusion Models on Inference Time
  Scaling
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.
68f6ea4a24c4489363111810
Erik Riise
claimed_verified
2025-10-21T14:04:09.278Z
647076753601bb7b06653691
/avatars/334940cbd07b084444b0eb227f88768d.svg
Erik Riise
user
Eririi
68f6ea4a24c4489363111811
Mehmet Onurcan Kaya
claimed_verified
2025-10-21T14:04:13.043Z
63be9021da08ed0544f36c38
/avatars/42511d8b1d3a3ef99bc154c98b72dfba.svg
onurcan
user
monurcan
68f6ea4a24c4489363111812
Dim P. Papadopoulos
Visual Autoregressive Models Beat Diffusion Models on Inference Time
  Scaling
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.
Beam search in discrete visual autoregressive models enhances text-to-image generation more effectively than search in continuous diffusion models, highlighting architecture's importance over scale.
visual autoregressive models beam search diffusion models discrete token space early pruning computational reuse verifier analysis