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Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
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Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
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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}.
Forward citations
Cited by 6 Pith papers
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LPDP adds a local re-solving operator to edit-flow DNA generators so that reward signals can guide insertions, deletions, and substitutions without retraining.
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MP2D: Constrained Monte Carlo Tree-Guided Diffusion for Multi-Objective Protein Sequence Design
MP2D is a framework that guides discrete diffusion denoising with constrained MCTS and Pareto rewards to optimize protein sequences for four to five simultaneous objectives, outperforming baselines on antimicrobial pe...
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On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization
Online fine-tuning of discrete diffusion models with complementary acquisition, CVaR shaping, density-entropy debiasing, replay, and validity control finds better molecules under fixed oracle budgets than offline fine...
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