4 citations · 5 across the 7 of their papers we have counts for
7 papers
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…
Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement
Meihua Dang, Jiaqi Han, Minkai Xu +3
Discrete diffusion models have recently emerged as strong alternatives to autoregressive language models, matching their performance through large-scale training. However, inferenc…
Divergence Minimization Preference Optimization for Diffusion Model Alignment
Binxu Li, Minkai Xu, Jiaqi Han +2
Diffusion models have achieved remarkable success in generating realistic and versatile images from text prompts. Inspired by the recent advancements of language models, there is a…
Discrete Diffusion Trajectory Alignment via Stepwise Decomposition
Jiaqi Han, Austin Wang, Minkai Xu +6
Discrete diffusion models have demonstrated great promise in modeling various sequence data, ranging from human language to biological sequences. Inspired by the success of RL in l…
Personalized Preference Fine-tuning of Diffusion Models
Meihua Dang, Anikait Singh, Linqi Zhou +2
RLHF techniques like DPO can significantly improve the generation quality of text-to-image diffusion models. However, these methods optimize for a single reward that aligns model g…
Structure-Based Drug Design via 3D Molecular Generative Pre-training and Sampling
Yuwei Yang, Siqi Ouyang, Xueyu Hu +3
Structure-based drug design aims at generating high affinity ligands with prior knowledge of 3D target structures. Existing methods either use conditional generative model to learn…
Diffusion Model Alignment Using Direct Preference Optimization
Bram Wallace, Meihua Dang, Rafael Rafailov +7
Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' prefe…