Curriculum-Augmented GFlowNets For mRNA Sequence Generation

We develop an approach for the de novo synthesis of messenger RNA (mRNA) sequences encoding target proteins. This requires optimizing multiple biological objectives over large combinatorial spaces defined by synonymous codon sequences, a setting where Reinforcement Learning (RL) methods suffer from mode collapse and Generative Flow Networks (GFlowNets) face sparse rewards over long horizons. To address these challenges, we propose Curriculum-Augmented GFlowNets (CAGFNs), a multi-objective GFlowNet trained with an adaptive length-based curriculum driven by learning progress. In a nutshell, CAGFNs learn from progressively harder tasks corresponding to longer target proteins. We evaluate CAGFNs on CodonDesignEnv, a novel codon-level environment that preserves protein identity via dynamic action masking. Our experiments show that CAGFNs achieve competitive or stronger Pareto-front coverage and diversity while converging up to 4 times faster than non-curriculum GFlowNets, and maintaining 100% sequence uniqueness compared to RL baselines.