Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design

• URL: http://arxiv.org/abs/2502.14944v1
• Date: Thu, 20 Feb 2025 17:48:45 GMT
• Title: Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
• Authors: Masatoshi Uehara, Xingyu Su, Yulai Zhao, Xiner Li, Aviv Regev, Shuiwang Ji, Sergey Levine, Tommaso Biancalani,
• Abstract summary: We propose a novel framework for inference-time reward optimization with diffusion models inspired by evolutionary algorithms.<n>Our approach employs an iterative refinement process consisting of two steps in each iteration: noising and reward-guided denoising.
• Score: 87.58981407469977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To fully leverage the capabilities of diffusion models, we are often interested in optimizing downstream reward functions during inference. While numerous algorithms for reward-guided generation have been recently proposed due to their significance, current approaches predominantly focus on single-shot generation, transitioning from fully noised to denoised states. We propose a novel framework for inference-time reward optimization with diffusion models inspired by evolutionary algorithms. Our approach employs an iterative refinement process consisting of two steps in each iteration: noising and reward-guided denoising. This sequential refinement allows for the gradual correction of errors introduced during reward optimization. Besides, we provide a theoretical guarantee for our framework. Finally, we demonstrate its superior empirical performance in protein and cell-type-specific regulatory DNA design. The code is available at \href{https://github.com/masa-ue/ProDifEvo-Refinement}{https://github.com/masa-ue/ProDifEvo-Refinement}.

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