DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs

ArXi:2606.01024v1 Announce Type: cross Discrete Masked diffusion language models generate text by iterative parallel decoding, but few-step decoding suffers from a tradeoff between length and quality: with a fixed step budget, standard methods can generate a short, high-quality output, or they can produce long but repetitive text. Continuous denoising can sidestep this tradeoff by evolving all positions jointly in embedding space, but building such a model from scratch at scale remains an open problem.