sCM-RNA: Preference-Aligned Consistency Models for RNA Inverse Folding

RNA inverse folding aims to design sequences that fold into prescribed backbone structures, supporting a wide range of applications in RNA engineering. Sequence recovery measures agreement with native sequences, but provides an incomplete assessment of structural fidelity and sequence diversity. Designing diverse sequences with high structural fidelity therefore calls for objectives that evaluate their predicted folds, together with efficient generation. We introduce **sCM-RNA**, an RNA inverse folding framework that combines continuous-time consistency modeling with preference-based alignment. The consistency model enables one-step sequence generation conditioned on target backbone structures, while the alignment stage uses preferences based on predicted structural fidelity to guide generation and explicitly promote sequence diversity. Together, these components support efficient sampling of diverse sequences whose predicted structures closely match the targets. Across six random seeds, sCM-RNA improves TM-score by **2.3%** and sequence diversity by **10.9%** relative to the strongest evaluated baseline for each metric, while achieving the highest sequence recovery. With one-step inference, sCM-RNA also achieves up to **35$\times$** faster sequence generation, supporting large-scale computational RNA design.