Byte finally released the diffusion language model, with 23B parameters performing text generation in the latent space; the approach is quite bold.

ByteDance Seed open-source Cola DLM is a model that performs text diffusion at the potential semantic level.
Text VAE maps text to a continuous latent space, while block-causal DiT learns the latent prior through Flow Matching, ultimately with a conditional decoder restoring the latent variables back into text.
Total parameters are approximately 2.3 billion (DiT 1.8 billion, VAE 500 million).
In 8 evaluation tasks, it competes with and outperforms baseline AR/LLaDA models of similar scale, but it is still a research checkpoint, not fine-tuned with instructions or RLHF.
The current repository only includes the text pipeline, with future plans to extend to text-image.