Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design

Researchers propose a new iterative distillation framework for fine-tuning diffusion models in biomolecular design that optimizes for specific reward functions. The method addresses stability and efficiency issues in existing reinforcement learning approaches by using off-policy data collection and KL divergence minimization for improved training stability.