Adaptive Inference-Time Scaling via Cyclic Diffusion Search

• URL: http://arxiv.org/abs/2505.14036v3
• Date: Sat, 05 Jul 2025 09:50:15 GMT
• Title: Adaptive Inference-Time Scaling via Cyclic Diffusion Search
• Authors: Gyubin Lee, Truong Nhat Nguyen Bao, Jaesik Yoon, Dongwoo Lee, Minsu Kim, Yoshua Bengio, Sungjin Ahn,
• Abstract summary: We introduce the challenge of adaptive inference-time scaling-dynamically adjusting computational effort during inference.<n>We propose Adaptive Bi-directional Cyclic Diffusion (ABCD), a flexible, search-based inference framework.<n>ABCD refines outputs through bi-directional diffusion cycles while adaptively controlling exploration depth and termination.
• Score: 68.58892778987936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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