The problem with that is we want the model to learn to deal with its own mistakes. With continuous diffusion mistakes mostly look like noise, but with what you’re proposing mistakes are just incorrect words that are semantically pretty similar to the real text, so the model wouldn’t learn to consider those “noise”. The noising function would have to generate semantically similar text (e.g., out of order correct tokens maybe? Tokens from a paraphrased version?)