ReSTFlow: Stable Few-Step Rectified Flow for RNA Backbone Generation

RNA molecules play central roles in biological regulation and therapeutic development, with many of their functions closely tied to three-dimensional structure. Recent diffusion and flow-based generative models provide effective frameworks for unconditional RNA backbone generation. However, existing approaches often require many sampling steps to maintain steric validity, limiting their efficiency in large-scale candidate generation. Here, we introduce ReSTFlow, a rectified flow framework that integrates steric regularization and trajectory correction for few-step, high-quality RNA backbone generation. The first stage trains STFlow, a sterically regularized flow model that represents each nucleotide as a local rigid frame and learns continuous transport from a noise distribution to RNA backbone conformations under clash-aware supervision. In the second stage, the learned flow is rectified using trajectories constructed from STFlow-generated samples, resulting in straighter transport paths that can be accurately integrated with substantially fewer sampling steps. At inference, trajectory correction stabilizes coarse translational updates during sampling. With 20 sampling steps, ReSTFlow achieves 75.0% steric validity, and 69.8% of its sterically valid backbones are designable, demonstrating strong performance in the few-step setting. These results establish ReSTFlow as an effective framework for high-quality RNA backbone generation under limited sampling budgets.