A Bidirectional Generative DNA Model with Motif-Inpainting Policy Optimization

Early DNA foundation models adopted BERT-style training, achieving strong performance on sequence understanding tasks but offering limited support for generation. Recent autoregressive models enable DNA generation, yet their left-to-right factorization does not naturally reflect the bidirectional dependencies underlying gene regulation. We present D3LM, which unifies both capabilities through masked diffusion. D3LM retains the Nucleotide Transformer (NT) architecture while reformulating its training objective as discrete masked diffusion. It retains NT's sequence understanding capabilities while generating regulatory sequences that more closely resemble real DNA than those produced by the evaluated autoregressive baselines. Building on D3LM's bidirectional generation capabilities, we introduce Motif-Inpainting Policy Optimization (MIPO) for cell-specific regulatory sequence design. MIPO anchors regulatory motifs within high-scoring sequences and inpaints their local neighborhoods while preserving the outer context, using functional feedback to select improved candidates for reinforcement learning. Our results highlight masked diffusion as a promising framework for unified DNA understanding, generation, and functional design.