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Constrained Decoding for Diffusion Language Models via Efficient Inference over Finite Automata
Meihua Dang, Stefano Ermon
Constrained decoding is essential for serving LLMs, ensuring that generated outputs follow specific structures such as JSON schema-formatted function calls. Existing systems are de…
One-Step Generative Modeling via Wasserstein Gradient Flows
Jiaqi Han, Puheng Li, Qiushan Guo +3
Diffusion models and flow-based methods have shown impressive generative capability, especially for images, but their sampling is expensive because it requires many iterative updat…
Generative Modeling with Flux Matching
Peter Pao-Huang, Xiaojie Qiu, Stefano Ermon
We introduce Flux Matching, a new paradigm for generative modeling that generalizes existing score-based models to a broader family of vector fields that need not be conservative.…
Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models
Austin Wang, Jiaqi Han, Stefano Ermon +1
Preference optimization has emerged as an efficient alternative to online reinforcement learning from human feedback (RLHF) for aligning text-to-image diffusion models. However, ex…
Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics
Aniketh Iyengar, Jiaqi Han, Pengwei Sun +3
Generating molecular dynamics (MD) trajectories using deep generative models has attracted increasing attention, yet remains inherently challenging due to the limited availability…
ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
Gabe Guo, Thanawat Sornwanee, Lutong Hao +3
Generating continuous-time, continuous-space stochastic processes (e.g., videos, weather forecasts) conditioned on partial observations (e.g., first and last frames) is a fundament…