Next-Concept Modeling is claimed to be up to 31× faster than similar-sized diffusion models on summarization and translation

The team behind Next-Concept Modeling says its parallel, coarse-to-fine approach generated text up to 31× faster than similarly sized diffusion language models on tasks including summarization and machine translation, with comparable or better quality. It also reports 10–21× faster unconditional generation across TinyStories, LM1B and OpenWebText while using 3–5× fewer training tokens. The team says NCM outperformed autoregressive and diffusion baselines on Sudoku with fewer sampling steps.