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New MDM-VGB sampler enhances diffusion models with reward-guided remasking

By PulseAugur Editorial · [2 sources] ·

Researchers have developed MDM-VGB, a novel discrete diffusion sampler designed to enhance Masked Diffusion Models (MDMs). This new method integrates reward-guided remasking, drawing inspiration from the Jerrum-Sinclair backtracking Markov chain, to improve both high-reward generation and sample editing. MDM-VGB operates on a masked-state graph, allowing for flexible unmasking and remasking of tokens to favor higher-value configurations. The approach is theoretically robust and achieves quadratic complexity, outperforming heuristics like best-of-N, with empirical validation on benchmarks such as Sudoku and QM9. AI

IMPACT Introduces a more efficient method for reward satisfaction and sample editing in diffusion models, potentially improving performance on constraint-satisfaction tasks.

RANK_REASON The cluster contains an academic paper detailing a new method for diffusion models.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New MDM-VGB sampler enhances diffusion models with reward-guided remasking

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The cluster contains an academic paper detailing a new method for diffusion models.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Kijung Jeon, Thuy-Duong Vuong, Molei Tao ·

    VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing

    arXiv:2606.28301v1 Announce Type: cross 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-VG…

  2. arXiv stat.ML TIER_1 English(EN) · Molei Tao ·

    VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing

    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 unma…

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