Controlled DNA Design via Rollout Reasoning with Discrete Flow Matching

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 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 demonstrate 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.