Curriculum-Augmented GFlowNets For mRNA Sequence Generation
This work introduces Curriculum-Augmented GFlowNets (CAGFNs) to design new messenger RNA sequences by optimizing multiple biological goals. It combines Generative Flow Networks with curriculum learning, where the model learns progressively harder tasks corresponding to longer target proteins. This adaptive training structure leads to faster convergence and better diversity in sequence generation compared to standard methods.