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Seq-Drifting enables one-step text generation in language models

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Seq-Drifting, a novel framework for language models that enables one-step text generation. This method applies a "drifting" mechanism to sequences of continuous token representations, learning a direct mapping from Gaussian noise to text. Unlike traditional diffusion models that require multiple sampling steps, Seq-Drifting generates all tokens jointly in a single forward pass. The framework achieves competitive generative perplexity on LM1B and demonstrates broad applicability across tasks like translation, summarization, and reasoning. AI

IMPACT This new method could significantly speed up text generation processes for various AI applications.

RANK_REASON The cluster contains an academic paper detailing a new method for language model text generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Seq-Drifting enables one-step text generation in language models

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The cluster contains an academic paper detailing a new method for language model text generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiayu Liu, Ruizhi Zhao, Tianyun Ji, Yee Whye Teh, Zhenya Huang, Ning Miao ·

    One-Step Text Generation by Seq-Drifting

    arXiv:2610.12879v1 Announce Type: new Abstract: Diffusion language models generate tokens in parallel, but still require multiple sampling steps to produce high-quality text. In this paper, we investigate whether a model can learn to generate an entire sequence in a single forwar…

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