Interacting particle guidance for sampling reward-tilted generative priors

arXiv:2609.37227 · cs.LG, stat.ML · Submitted 2026-09-29 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Trace — 10× in this paper · explained in 2 episodes
• Regularization — 8× in this paper · explained in 3 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Generative Model — 6× in this paper · explained in 2 episodes
• Inverse Problem — 6× in this paper · explained in 2 episodes
• Degeneracy — 5× in this paper · explained in 3 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Steering — 5× in this paper · explained in 2 episodes
• Inference-Time Steering — 4× in this paper · explained in 2 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Hilbert Space — 3× in this paper · explained in 2 episodes
• Reward Function — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• Sequential Monte Carlo (SMC) — 2× in this paper · explained in 2 episodes
• 2-Wasserstein Distance — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Computational Biology — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Langevin Dynamics — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• Stochastic Gradient MCMC with Repulsive Forces
• ESS-Flow: Training-free guidance of flow-based models as inference in source space
• Stein transport for Bayesian inference
• It\^o maps for any-step SDEs

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