6 papers · 1 filter
Rethinking the Design Space of Reinforcement Learning for Diffusion Models: On the Importance of Likelihood Estimation Beyond Loss Design
Jaemoo Choi, Yuchen Zhu, Wei Guo +6
Reinforcement learning has been widely applied to diffusion and flow models for visual tasks such as text-to-image generation. However, these tasks remain challenging because diffu…
Proximal Diffusion Neural Sampler
Wei Guo, Jaemoo Choi, Yuchen Zhu +2
The task of learning a diffusion-based neural sampler for drawing samples from an unnormalized target distribution can be viewed as a stochastic optimal control problem on path mea…
Efficient Adjoint Matching for Fine-tuning Diffusion Models
Jeongwoo Shin, Dongsoo Shin, Yuchen Zhu +5
Reward fine-tuning has become a common approach for aligning pretrained diffusion and flow models with human preferences in text-to-image generation. Among reward-gradient-based me…
Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms
Yinuo Ren, Haoxuan Chen, Yuchen Zhu +5
Discrete diffusion models have emerged as a powerful generative modeling framework for discrete data with successful applications spanning from text generation to image synthesis.…
MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control
Yuchen Zhu, Wei Guo, Jaemoo Choi +3
We study the problem of learning a neural sampler to generate samples from discrete state spaces where the target probability mass function is known up t…
Plug-and-Play Controllable Generation for Discrete Masked Models
Wei Guo, Yuchen Zhu, Molei Tao +1
This article makes discrete masked models for the generative modeling of discrete data controllable. The goal is to generate samples of a discrete random variable that adheres to a…