Learning Unmasking Policies for Diffusion Language Models

Research on Diffusion Language Models (dLLMs) shows that manual heuristic unmasking strategies, like confidence thresholding, are suboptimal. The study explores learning unmasking policies to improve both sample quality and token throughput during inference.