Looping Improves Uniform Diffusion Language Model Efficiency

Applying selective looping to Uniform Diffusion Language Models (UDLMs) improves training efficiency under iso-FLOP budgets. While UDLMs are more compute-intensive than masked diffusion models due to denoising from unreliable contexts, recursive application of early Transformer layers optimizes performance.