Developing effective training and deployment recipes for diffusion-based language models requires work across data, evaluation, reinforcement learning, post-training infrastructure, training, inference, and engineering.

“possibilities for training compute for quality at inference time like even if you think about image diffusion models or video diffusion models there is a lot of techniques that you can use to kind of like accelerate sampling of distillation or like fancy differential equation solving techniques that allow you to sample very very quickly from these models and so there is a lot of research on the training on the inference and then engineering like just”

“Just like thinking about data mixes,”

“evals, RL, post-training infrastructure,”

“like there is a lot of work that needs to happen to figure out how to build the recipes that work for this new model.”