DREAM-DNA: Controlled Design via Reasoning and Matched-flows for DNA

We introduce DREAM-DNA (controlled Design via REasoning And Matched-flows for DNA), a reinforcement learning–based generative framework for DNA sequence design. Traditional models in this domain use diffusion architecture and rely on stochastic, single-pass generation. These limit their ability to correct early structural errors. DREAM-DNA overcomes these limitations by replacing stochasticity with Discrete Flow Matching. This establishes a deterministic ''straight-line'' mapping from noise to data for superior trajectory control. Our framework further introduces an Iterative Rollout mechanism. Specifically, we treat generation as a multi-round refinement process. By iteratively masking and resampling subsets of nucleotides in the DNA sequence, the model ``critiques'' and revises its intermediate outputs based on the reward feedback. Experiments on human enhancer design show that DREAM-DNA outperforms state-of-the-art baselines. It achieves a 13% boost in enhancer activity and an 8% increase in open chromatin match levels.