Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. However, most inference-time scaling methods rely on fixed denoising schedules, limiting their ability to allocate computation based on instance difficulty or task-specific demands adaptively. We introduce the challenge of adaptive inference-time scaling-dynamically adjusting computational effort during inference-and propose Adaptive Bi-directional Cyclic Diffusion (ABCD), a flexible, search-based inference framework. ABCD refines outputs through bi-directional diffusion cycles while adaptively controlling exploration depth and termination. It comprises three components: Cyclic Diffusion Search, Automatic Exploration-Exploitation Balancing, and Adaptive Thinking Time. Experiments show that ABCD improves performance across diverse tasks while maintaining computational efficiency.
@article{arxiv.2505.14036,
title = {Adaptive Inference-Time Scaling via Cyclic Diffusion Search},
author = {Gyubin Lee and Truong Nhat Nguyen Bao and Jaesik Yoon and Dongwoo Lee and Minsu Kim and Yoshua Bengio and Sungjin Ahn},
journal= {arXiv preprint arXiv:2505.14036},
year = {2025}
}