One-Step Text Generation by Seq-Drifting
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 forward pass.
This paper introduces Seq-Drifting, a framework for generating entire text sequences in a single forward pass using diffusion language models, addressing efficiency limitations in current text generation methods.
What happened
Researchers developed Seq-Drifting, a method that trains models to generate complete text sequences in a single inference step by using a drift field that dynamically adjusts token representations during training, avoiding repetition and collapse through attraction/repulsion forces.
Why it matters
This approach could significantly improve the efficiency of text generation systems, benefiting applications requiring real-time or resource-constrained environments while maintaining high-quality outputs.
- One-step generation is achievable through advanced diffusion model design
- Dynamic drift fields improve contextual coherence and diversity in generated text
- The method shows versatility across different NLP tasks beyond open-ended generation
The bigger picture
The framework demonstrates potential for reducing computational costs in text generation tasks while maintaining performance across multiple NLP applications.
Seq-Drifting presents a novel approach to text generation by enabling single-pass inference through adaptive drift field mechanisms, achieving competitive results while addressing efficiency challenges in diffusion language models.
Technical details
- Drift field: Position-wise targets that adapt to generated context during training
- Attraction mechanism: Guides outputs toward contextually plausible token representations
- Repulsion mechanism: Prevents sample collapse and sequence repetition
- Single-pass inference: Generates all tokens simultaneously rather than sequentially
FAQ
What problem does Seq-Drifting solve?
It addresses the inefficiency of traditional diffusion language models that require multiple sampling steps for high-quality text generation.
How does the drift field work?
The drift field uses attraction forces to guide tokens toward contextually appropriate representations and repulsion forces to prevent repetition and sample collapse.
What are the key performance metrics?
The framework achieved a generative perplexity of 36.01 on LM1B and demonstrated competitive performance in translation, summarization, and reasoning tasks.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - cs.AI on Oct 12, 2026.
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