ALoDLM Uses Adaptive Latent Recurrence for Efficient Diffusion Language Models

ALoDLM closes the quality gap between diffusion language models and autoregressive models by replacing uniform computation with token-adaptive latent recurrence. It allocates computational depth based on token difficulty, iteratively refining representations for harder tokens while committing easier ones earlier.