Scaling Behavior of Discrete Diffusion Language Models

This article likely discusses the performance characteristics of discrete diffusion models as they are scaled up in size and computational resources. It would analyze how model performance (e.g., accuracy, fluency) changes with increasing parameters, training data, and compute. The 'scaling behavior' refers to the relationship between these factors and the model's capabilities. The ArXiv source suggests this is a research paper, focusing on technical details and experimental results.