D. Roos

On the Design Space of Continuous Diffusion Language Models

It is shown that self-conditioning corrects a train/inference mismatch by exposing the model to inference-like states that are otherwise rare during training, and cross-entropy drives embeddings towards a geometry whose decoding error rate closely matches the one-hot one.