Discrete Flow Matching Policy Optimization

Tue, 07 Apr 2026 00:00:00 +0000

DoMinO is a reinforcement learning framework for fine-tuning discrete flow-matching models. It formulates discrete flow-matching inference as an inner Markov decision process, which enables exact trajectory log-likelihood optimization. Fine-tuning uses REINFORCE- and PPO-based methods with cross-entropy and generalized KL regularization to mitigate reward hacking and preserve sequence naturalness.

On regulatory DNA and protein design, DoMinO outperforms prior state-of-the-art baselines by 10.6% on DNA predicted activity and 15.4% on protein pred-ddG.

My role: project co-lead. See the .

M. Lu | Po-Chung (Bryan) Hsieh

Discrete Flow Matching Policy Optimization