This paper introduces a novel approach to learning unmasking policies for diffusion language models. The research proposes a method that automatically learns optimal token selection strategies, moving beyond manual heuristics to improve both sample quality and inference efficiency in dLLMs. The findings suggest that learned policies can outperform fixed strategies, offering a more adaptable and performant solution for dLLM inference.
Opening Kapyn…