Rewardguided Iterative Refinement In Diffusion Models At Testtime With Applications To Protein And Dna Design

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

**arXiv ID:** 2502.14944
**Authors:** Masatoshi Uehara, Xingyu Su, Yulai Zhao, Xiner Li, Aviv Regev, Shuiwang Ji, Sergey Levine, Tommaso Biancalani
**Published:** 2025-02-20T17:48:45Z
**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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## References

- [arXiv:2502.14944](http://arxiv.org/abs/2502.14944v1)