Discrete Flow Matching Policy Optimization
DoMinO: A New Frontier for Controllable Discrete Sequence Generation in Enterprise AI
Discrete Flow Matching policy Optimization (DoMinO) is a unified framework for Reinforcement Learning (RL) fine-tuning Discrete Flow Matching (DFM) models. It reframes DFM sampling as a multi-step Markov Decision Process (MDP) for robust RL objective formulation, avoiding biased estimators. DoMinO introduces total-variation regularizers to maintain naturalness and establishes theoretical bounds for discretization errors and regularizers. Experimental results on regulatory DNA sequence design show superior enhancer activity and sequence naturalness compared to baselines, affirming its utility for controllable discrete sequence generation.
Executive Impact
DoMinO introduces a novel Reinforcement Learning framework for fine-tuning Discrete Flow Matching (DFM) models, specifically designed for discrete sequence generation tasks like DNA design. By reinterpreting DFM sampling as a Markov Decision Process, DoMinO leverages standard policy gradient methods while avoiding common pitfalls of biased estimators. The inclusion of total-variation regularizers ensures generated sequences remain natural and close to the original data distribution. This approach has demonstrated state-of-the-art performance in regulatory DNA sequence design, offering a powerful tool for controllable and natural discrete sequence generation in enterprise AI applications such as drug discovery and synthetic biology.
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Methodology
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DoMinO Framework Overview
DoMinO reframes DFM inference as an inner Multi-step Markov Decision Process (MDP) to apply policy gradient methods for reward maximization.
Pre-trained DFM Model
→
DFM Inference Reimagined as MDP
→
Policy Gradient Optimization (REINFORCE/PPO)
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Total Variation Regularization
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Fine-tuned DFM for Reward Max.
| Feature | DoMinO | Prior Methods (e.g., DRAKES, SEPO) |
|---|---|---|
| Exact Policy Likelihood | Tractable via DFM one-step transition | Intractable; relies on approximations/surrogates |
| Bias in Estimators | Unbiased | Often biased due to auxiliary estimators |
| Non-differentiable Rewards | Directly supported | Requires tricks (Gumbel-Softmax) or approximations |
| Preservation of Naturalness | Total Variation (TV) regularizers | Path-wise KL regularization (less flexible) |
Enhanced Enhancer Activity
DoMinO achieves state-of-the-art predicted enhancer activity on the HepG2 cell line, demonstrating superior functional performance.
8.35 Pred-Activity Score (Higher is Better)Improved Sequence Naturalness
With regularization, DoMinO significantly improves sequence naturalness, achieving the only positive 3-mer correlation among tested methods.
0.013 3-mer Corr-All (Positive is Better)Regulatory DNA Sequence Design
DoMinO was validated on the task of designing regulatory DNA sequences for the HepG2 cell line. It successfully generates sequences with higher predicted enhancer activity and better naturalness, demonstrating its potential for synthetic biology and therapeutic design.
Challenge: Designing synthetic DNA sequences with desired functional properties while maintaining biological realism (naturalness).
Solution: DoMinO's RL fine-tuning, coupled with TV regularization, effectively navigates the trade-off between functional optimization and sequence naturalness.
Impact: Generated sequences exhibit stronger enhancer activity and higher alignment with natural sequence distributions, providing a powerful tool for accelerating drug discovery and gene therapy development.
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