ScalingAR: Scaling Confidence for Autoregressive Image Generation

Harold Haodong Chen, Xianfeng Wu, Wen-Jie Shu, Rongjin Guo, Disen Lan, Harry Yang, Ying-Cong Chen
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:17508-17526, 2026.

Abstract

Test-time strategies have shown remarkable success in improving large language models, but their application to next-token prediction (NTP) autoregressive (AR) image generation remains largely underexplored. Existing test-time scaling (TTS) methods for visual autoregressive models (VAR) rely on frequent partial decoding and external reward models, which are inefficient and often ineffective for NTP-based image generation due to the inherent instability of intermediate decoding results. To address these limitations, we propose ScalingAR, a novel test-time scaling framework tailored for NTP-based AR image generation. ScalingAR introduces token entropy as a confidence signal and operates at two complementary levels: (i) Profile Level, integrates intrinsic uncertainty and conditional utilization into a unified confidence state, and (ii) Policy Level, leverages this state for adaptive trajectory pruning and dynamic guidance scheduling. Without requiring early decoding or auxiliary rewards, ScalingAR achieves significant improvements across diverse benchmarks. Experiments show that ScalingAR (I) improves base models by 12.5% on GenEval and 15.2% on TIIF-Bench, (II) reduces visual token consumption by 62.0% while outperforming baselines, and (III) enhances robustness, mitigating performance degradation by 26.0% in challenging scenarios. These results establish ScalingAR as a robust and efficient test-time scaling solution for autoregressive image generation.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-chen26fc, title = {{S}caling{AR}: Scaling Confidence for Autoregressive Image Generation}, author = {Chen, Harold Haodong and Wu, Xianfeng and Shu, Wen-Jie and Guo, Rongjin and Lan, Disen and Yang, Harry and Chen, Ying-Cong}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {17508--17526}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/chen26fc/chen26fc.pdf}, url = {https://proceedings.mlr.press/v306/chen26fc.html}, abstract = {Test-time strategies have shown remarkable success in improving large language models, but their application to next-token prediction (NTP) autoregressive (AR) image generation remains largely underexplored. Existing test-time scaling (TTS) methods for visual autoregressive models (VAR) rely on frequent partial decoding and external reward models, which are inefficient and often ineffective for NTP-based image generation due to the inherent instability of intermediate decoding results. To address these limitations, we propose ScalingAR, a novel test-time scaling framework tailored for NTP-based AR image generation. ScalingAR introduces token entropy as a confidence signal and operates at two complementary levels: (i) Profile Level, integrates intrinsic uncertainty and conditional utilization into a unified confidence state, and (ii) Policy Level, leverages this state for adaptive trajectory pruning and dynamic guidance scheduling. Without requiring early decoding or auxiliary rewards, ScalingAR achieves significant improvements across diverse benchmarks. Experiments show that ScalingAR (I) improves base models by 12.5% on GenEval and 15.2% on TIIF-Bench, (II) reduces visual token consumption by 62.0% while outperforming baselines, and (III) enhances robustness, mitigating performance degradation by 26.0% in challenging scenarios. These results establish ScalingAR as a robust and efficient test-time scaling solution for autoregressive image generation.} }
Endnote
%0 Conference Paper %T ScalingAR: Scaling Confidence for Autoregressive Image Generation %A Harold Haodong Chen %A Xianfeng Wu %A Wen-Jie Shu %A Rongjin Guo %A Disen Lan %A Harry Yang %A Ying-Cong Chen %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-chen26fc %I PMLR %P 17508--17526 %U https://proceedings.mlr.press/v306/chen26fc.html %V 306 %X Test-time strategies have shown remarkable success in improving large language models, but their application to next-token prediction (NTP) autoregressive (AR) image generation remains largely underexplored. Existing test-time scaling (TTS) methods for visual autoregressive models (VAR) rely on frequent partial decoding and external reward models, which are inefficient and often ineffective for NTP-based image generation due to the inherent instability of intermediate decoding results. To address these limitations, we propose ScalingAR, a novel test-time scaling framework tailored for NTP-based AR image generation. ScalingAR introduces token entropy as a confidence signal and operates at two complementary levels: (i) Profile Level, integrates intrinsic uncertainty and conditional utilization into a unified confidence state, and (ii) Policy Level, leverages this state for adaptive trajectory pruning and dynamic guidance scheduling. Without requiring early decoding or auxiliary rewards, ScalingAR achieves significant improvements across diverse benchmarks. Experiments show that ScalingAR (I) improves base models by 12.5% on GenEval and 15.2% on TIIF-Bench, (II) reduces visual token consumption by 62.0% while outperforming baselines, and (III) enhances robustness, mitigating performance degradation by 26.0% in challenging scenarios. These results establish ScalingAR as a robust and efficient test-time scaling solution for autoregressive image generation.
APA
Chen, H.H., Wu, X., Shu, W., Guo, R., Lan, D., Yang, H. & Chen, Y.. (2026). ScalingAR: Scaling Confidence for Autoregressive Image Generation. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:17508-17526 Available from https://proceedings.mlr.press/v306/chen26fc.html.

Related Material