Stanford team's Next-Concept Modeling generates text 10-31x faster
The result suggests the next speed leap may come from changing generation granularity rather than from bigger GPUs: drafting concepts first, then filling in tokens. Researchers and engineers comparing sampling strategies for diffusion and autoregressive models can benchmark NCM against baselines like diffusion language models using far fewer training tokens. As it comes from the Stanford Hazy Research group, expect open follow-ups and code to appear in the open-source model ecosystem.
Researchers at Stanford AI Lab including Hermann Kumbong and Ishan Khare introduced Next-Concept Modeling (NCM), a parallel coarse-to-fine text generation framework. Compared with diffusion language models of similar parameter size, NCM is up to 31x faster on summarization and machine translation and 10-21x faster on unconditional generation across TinyStories, LM1B and OpenWebText. It matches or beats diffusion baselines on generative perplexity while using 3-5x fewer training tokens. On reasoning tasks such as Sudoku, NCM outperforms both autoregressive and diffusion baselines with fewer sampling steps.
What if language models wrote the way that we think through ideas: rough sketch first with details last? New work by @KumbongHermann , @ishanskhare , and team introduces Next-Concept Modeling (NCM), a parallel generation framework achieving comparable or better quality than strong diffusion baselines while being 10-31× faster⚡️across unconditional generation, summarization & translation, and downstream reasoning tasks. Hermann Kumbong @KumbongHermann 🚀 Introducing Next-Concept Modeling (NCM): our new paradigm for fast, parallel, coarse-to-fine text generation. Compared to strong diffusion language models (DLMs) at similar parameter size, NCM achieves: ⚡ Up to 31× faster generation on downstream tasks like summarization and machine translation with comparable or better quality. 🏎️ 10–21× faster unconditional generation across TinyStories, LM1B, and OpenWebText, with comparable or better generative perplexity and using 3–5× fewer training tokens. 🧩 Better performance on reasoning tasks like Sudoku, outperforming both AR and diffusion baselines with fewer sampling steps. With @ishanskhare , @IddahMlauzi , @ekellbuch , @simran_s_arora , @Azaliamirh , and @HazyResearch . More details in the thread 🧵👇 Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 2 🔄 7 ❤️ 12 👀 1115 📊 5 ⚡
- arXiv cs.AI10-15 17:52原文
