Prefilling-dLLM v2: Learned Chunk Routing for Long-Context Diffusion Language Models
Diffusion language models apply bidirectional attention over the whole context at every denoising step, so a longer context makes each step substantially more expensive. Sparsifying only at inference leaves the pretrained weights unchanged, so the model is never trained under the sparse pattern it runs with. We propose Prefilling-dLLM v2, which continues training a diffusion language model so that it routes with its native mask token. One mask is appended at the end of each chunk, and a learned role offset marks it as a routing slot. On every fourth layer, a query keeps a bidirectional local window and the top-K chunks scored from that mask; the other layers use a bidirectional sliding wi...
| Authors | Qi Han, Jing Xiong, Ziyuan Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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