RESEARCH · RESEARCH · #1847
Seq-Drifting: one-step text generation by drifting over token sequences (arXiv:2610.12879v1)
The paper proposes Seq-Drifting, a framework that learns a direct map from Gaussian noise to full token sequences in a single forward pass by building a position-wise drift field with attraction (to context-plausible token representations) and repulsion (to prevent sample collapse and repetition). The method achieves a one-step generative perplexity of 36.01 on LM1B, reports competitive unconditional and conditional generation, and is evaluated on translation, summarization, and reasoning; code is available at the authors' GitHub.
KEY POINTS
- The paper proposes Seq-Drifting, a framework that learns a direct map from Gaussian noise to full token sequences in a single forward pass by building a position-wise drift field with attraction (to context-plausible token representations) and repulsion (to prevent sample collapse and repetition).
- The method achieves a one-step generative perplexity of 36.01 on LM1B, reports competitive unconditional and conditional generation, and is evaluated on translation, summarization, and reasoning; code is available at the authors' GitHub.
- One-step generation that produces full sequences in one forward pass could substantially reduce sampling latency and point to a new class of efficient generative language models if results hold up.
WHY IT MATTERS
One-step generation that produces full sequences in one forward pass could substantially reduce sampling latency and point to a new class of efficient generative language models if results hold up.