Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models
Selects token commitments that most reduce uncertainty over the remaining output. Self-distillation reinforces these pivots from successful diffusion trajectories and discourages selected pivots from failed ones, rather than supervising every token equally.
First public: · Added to this list: October 18, 2026
Cite this paper
ArXiv preprint
arXiv:2610.03665 · Initial submission: 2026-10-02
A published-version citation has not been verified for this entry. This does not mean the paper is unpublished.
Choose the version you used. The arXiv link may serve a revision; check the original paper for the exact result, assumptions and limitations you cite.
Explore this approach
Alternative generation methods — Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. These are related approaches: some still produce written chains of thought.
Read or reuse this entry
Plain-text Markdown · JSON record · Find in the full list
Our summary and categories are a reading aid, not the authors’ abstract. Use the original paper for research claims. Suggest a correction.