5 papers · 1 filter
Entropy Can Flow, or It Can Guide. Be Entropy. LEDFlow: Introducing Entropy-guided Generation Order into Uniform Discrete Flow
Tung Sum Thomas Kwok, Yidong Ouyang, Yingjia Wan +3
Uniform discrete flow permits repeated updates at every generation position. While continued revision supports correction of wrong tokens, it also exposes correct intermediate pred…
REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge
Yasi Zhang, Tianyu Chen, Mingyuan Zhou +3
Large language models (LLMs) are increasingly deployed as automated evaluators that assign numeric scores to model outputs, a paradigm known as LLM-as-a-Judge. However, standard Re…
Learning Regularization Functionals for Inverse Problems: A Comparative Study
Johannes Hertrich, Hok Shing Wong, Alexander Denker +16
In recent years, a variety of learned regularization frameworks for solving inverse problems in imaging have emerged. These offer flexible modeling together with mathematical insig…
Score Distillation Beyond Acceleration: Generative Modeling from Corrupted Data
Yasi Zhang, Tianyu Chen, Zhendong Wang +3
Learning generative models directly from corrupted observations is a long standing challenge across natural and scientific domains. We introduce Restoration Score Distillation (RSD…
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation
Tianyu Chen, Yasi Zhang, Zhendong Wang +3
Diffusion models have achieved remarkable success in generating high-resolution, realistic images across diverse natural distributions. However, their performance heavily relies on…