arXiv AI
DOHF: Online Diffusion Fine-tuning with Doob's $h$-transform Guidance
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Conditional Diffusion Guidance under Hard Constraint: A Stochastic Analysis Approach
arXiv:2602. 05533v3 Announce Type: replace Abstract: We study conditional generation in diffusion models under hard constraints, where generated samples must satisfy prescribed events with probability one.
arXiv AI
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MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.
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GRAS: Guided Reduced-Variance Proposals and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion
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arXiv Machine Learning
Sep 1
Reward-guided Fine-Tuning of One-Step Generative Models via Wasserstein Gradient Flow
arXiv:2608.29647v1 Announce Type: new Abstract: To mitigate the time complexity of generative models, one-step generative models have recently emerged through direct mapping from noise to data in a s...
arXiv Machine Learning
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FlowAWR: Online Adaptive Flow Reinforcement via Advantage-Weighted Rectification
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arXiv Machine Learning
Aug 26
Scaling Reinforcement Learning for Diffusion Models via Velocity Matching
arXiv:2608.23664v1 Announce Type: cross Abstract: Reward fine-tuning is becoming an important tool for adapting diffusion models to human preferences and task-specific objectives, but existing method...