Seq-Drifting generates complete text in one model pass
Seq-Drifting shows diffusion language models can generate an entire sequence in one forward pass, cutting out the multiple sampling steps normally used for higher-quality text. It learns a context-aware drift field over continuous token representations: attraction pulls outputs toward plausible tokens, while repulsion limits collapse and repetition. On LM1B, it reaches a generative perplexity of 36.01 in one step, with experiments also covering translation, summarization, and reasoning.