FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion Language Models

arXiv:2509.20624v5 Announce Type: replace-cross Abstract: Autoregressive language models (ARMs) deliver strong likelihoods, but are inherently serial: they generate one token per forward pass, which limits throughput and inflates latency for long sequences. Diffusion Language Models (DLMs) parallelize across positions and thus appear promising for language generation, yet standard discrete diffusion typically needs hundreds to thousands of model evaluations to reach high quality, trading serial