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ARXIV:2604.06491 · REINFORCEMENT LEARNING · SUBMITTED 09 APR · 20:10 UTC · FRESHNESS UNKNOWN
ARXIV:2604.06491REINFORCEMENT LEARNINGSUBMITTED 09 APR · 20:10 UTCFRESHNESS UNKNOWNMaojiang Su · Po-Chung Hsieh · Weimin Wu · Mingcheng Lu · Jiunhau Chen · Jerry Yao-Chieh Hu · +1 at arXiv
A unified RL framework for fine-tuning discrete flow matching models, improving controllable discrete sequence generation.
Opportunity summary
Pain A unified RL framework for fine-tuning discrete flow matching models, improving controllable discrete sequence generation.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A unified RL framework for fine-tuning discrete flow matching models, improving controllable discrete sequence generation. Our key idea is to view the DFM sampling procedure as a multi-step Markov Decision Process.
We introduce Discrete flow Matching policy Optimization (DoMinO), a unified framework for Reinforcement Learning (RL) fine-tuning Discrete Flow Matching (DFM) models under a broad class of policy gradient methods. Our key idea is to…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. DoMinO achieves stronger predicted enhancer activity and better sequence naturalness than the previous best reward-driven baselines.
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A unified RL framework for fine-tuning discrete flow matching models, improving controllable discrete sequence generation.
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10.48550/arXiv.2604.06491A unified RL framework for fine-tuning discrete flow matching models, improving controllable discrete sequence generation.
Abstract
We introduce Discrete flow Matching policy Optimization (DoMinO), a unified framework for Reinforcement Learning (RL) fine-tuning Discrete Flow Matching (DFM) models under a broad class of policy gradient methods. Our key idea is to view the DFM sampling procedure as a multi-step Markov Decision Process. This perspective provides a simple and transparent reformulation of fine-tuning reward maximization as a robust RL objective. Consequently, it not only preserves the original DFM samplers but also avoids biased auxiliary estimators and likelihood surrogates used by many prior RL fine-tuning methods. To prevent policy collapse, we also introduce new total-variation regularizers to keep the fine-tuned distribution close to the pretrained one. Theoretically, we establish an upper bound on the discretization error of DoMinO and tractable upper bounds for the regularizers. Experimentally, we evaluate DoMinO on regulatory DNA sequence design. DoMinO achieves stronger predicted enhancer activity and better sequence naturalness than the previous best reward-driven baselines. The regularization further improves alignment with the natural sequence distribution while preserving strong functional performance. These results establish DoMinO as an useful framework for controllable discrete sequence generation.
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PROBLEM
A unified RL framework for fine-tuning discrete flow matching models, improving controllable discrete sequence generation. Our key idea is to view the DFM sampling procedure as a multi-step Markov Decision Process.
METHOD
We introduce Discrete flow Matching policy Optimization (DoMinO), a unified framework for Reinforcement Learning (RL) fine-tuning Discrete Flow Matching (DFM) models under a broad class of policy gradient methods. Our key idea is to view the DFM sampling procedure as a multi-ste...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. DoMinO achieves stronger predicted enhancer activity and better sequence naturalness than the previous best reward-driven baselines.
WHY NOW
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A unified RL framework for fine-tuning discrete flow matching models, improving controllable discrete sequence generation. Our key idea is to view the DFM sampling procedure as a multi-step Markov Decision Process.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We introduce Discrete flow Matching policy Optimization (DoMinO), a unified framework for Reinforcement Learning (RL) fine-tuning Discrete Flow Matching (DFM) models under a broad class of policy gradient methods. Our key idea is to view the DFM sampling procedure as a multi-step Markov Decision Process.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. DoMinO achieves stronger predicted enhancer activity and better sequence naturalness than the previous best reward-driven baselines.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A unified RL framework for fine-tuning discrete flow matching models, improving controllable discrete sequence generation.
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Reinforcement Learning
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