用经典搜索方法提升扩散模型推理时的生成效果与效率
Inference-time Scaling of Diffusion Models through Classical Search
- 结合局部与全局搜索，高效探索生成空间
- 在规划、强化学习和图像生成中性能显著提升
- 适合需要高精度控制的生成任务研究者
经典搜索算法长期支撑现代人工智能。本文针对扩散模型推理时的控制难题——即在测试阶段适应多样化目标——提出基于经典搜索的新框架。该框架通过理论支持的退火Langevin MCMC实现局部搜索，并采用广度优先与深度优先树搜索进行高效全局探索。我们在规划、离线强化学习和图像生成等多个挑战性任务上进行了评估，结果表明，在所有任务中均实现了性能与效率的显著提升。这些发现表明，经典搜索为扩散模型的推理时扩展提供了原理严谨且实用的基础。项目页面见 https://diffusion-inference-scaling.github.io/。
原文摘要 · Abstract (English)
Classical search algorithms have long underpinned modern artificial intelligence. In this work, we tackle the challenge of inference-time control in diffusion models -- adapting generated outputs to meet diverse test-time objectives -- using principles from classical search. We propose a general framework that orchestrates local and global search to efficiently navigate the generative space. It employs a theoretically grounded local search via annealed Langevin MCMC and performs compute-efficient global exploration using breadth-first and depth-first tree search. We evaluate our approach on a range of challenging domains, including planning, offline reinforcement learning, and image generation. Across all tasks, we observe significant gains in both performance and efficiency. These results show that classical search provides a principled and practical foundation for inference-time scaling in diffusion models. Project page at https://diffusion-inference-scaling.github.io/.
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