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.