Diffusion Language Models: An Experimental Analysis

This study presents a systematic experimental analysis of Diffusion Language Models (DLMs), evaluating eight state-of-the-art models across various benchmarks including reasoning, coding, and translation. The analysis highlights the influence of inference-time factors such as denoising steps and context length on performance and computational efficiency, revealing distinct trade-offs that practitioners must consider when deploying DLMs. This research contributes to a clearer understanding of DLM capabilities, aiding developers in optimizing model selection and configuration for specific tasks.