Full-length mRNA Design with Reward-Guided Masked Diffusion Model Fine-Tuning

High-fitness mRNA design requires the simultaneous optimization of multiple application-critical properties, but the design principles governing this high- dimensional landscape remain poorly understood. This motivates the need for methods that provide systematic, controllable generation of mRNA sequences. To address this, we introduce T3PO-mRNA, a framework for computing reward- guided discrete diffusion trajectories that iteratively construct increasingly accurate approximations of the Pareto frontier. Our approach leverages tree search to identify high-reward sequence trajectories and uses these trajectories to fine-tune diffusion models on progressively stronger sequence buffers. We demonstrate that T3PO-mRNA effectively designs therapeutic mRNAs with optimized half-life and translation efficacy, enabling both improved multi-objective performance and efficient inference-time sampling over prior inference-time guidance methods