VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing
arXiv:2606.28301v1 Announce Type: new Abstract: Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize downstream rewards. We consider Masked Diffusion Model (MDM) and introduce MDM-VGB, a discrete diffusion sampler that augments unmasking generation with theoretically principled reward-guided remasking. Inspired by the recent success of the classical Jerrum-Sinclair backtracking Markov chain in reward-tilted generation, MDM-VGB extends the backtracking random walk from a fixed prefix tree to a masked
The paper builds on recent advancements in generative AI and diffusion models, pushing towards more efficient and controllable generation of outputs that satisfy constraints or optimize rewards.
This research is crucial for developing AI models that can generate more reliable and structurally sound outputs, which is vital for applications requiring high assurance or specific optimization goals.
The introduction of MDM-VGB provides a theoretically grounded method for improving masked diffusion models through reward-guided remasking, enhancing control and efficiency in AI generation.
- · AI researchers
- · Generative AI developers
- · Industries requiring constrained AI outputs (e.g., engineering, drug discovery)
- · Developers relying solely on less efficient generative methods
Improved generative AI models that can better meet specific structural or optimization criteria.
Accelerated development of AI agents capable of more refined and goal-oriented content creation or problem-solving.
Potential for new AI applications in design, simulation, and scientific discovery where precise adherence to constraints is paramount.
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Read at arXiv cs.LG