The paper introduces a new way to sample text from masked diffusion language models that is smarter and less greedy.
This paper introduces XDLM, a single model that blends two popular diffusion styles (masked and uniform) so it both understands and generates text and images well.
BatCoder teaches a code model to write both code and its documentation by doing a round trip: from code to docs and back to code.
Stable-DiffCoder is a code-focused diffusion language model that learns to write and edit programs by filling in masked pieces, not just predicting the next token.