This paper addresses the decoding efficiency challenge in diffusion language models (DLMs), which have emerged as promising alternatives to autoregressive models for language generation. The research focuses on confidence-based decoding strategies that adaptively select and unmask tokens based on prediction confidence during the generation process. The authors develop a theoretical analysis framework specifically for an entropy sum-based strategy that continues unmasking tokens until cumulative entropy exceeds a threshold. This work provides the first formal theoretical guarantees demonstrating the efficiency of confidence-based decoding in diffusion-based text generation, offering insights into how adaptive token selection can improve sampling efficiency compared to traditional decoding approaches.