# Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models

Authors: Seo Hyun Kim, Sunwoo Hong, Younwoo Choi, Chen-Hao Chao, Se-Young Yun, Rahul G. Krishnan.

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: 2026-10-02.
ArXiv initial submission: 2026-10-02.

[Original paper](https://arxiv.org/abs/2610.03665) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.03665/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.03665/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.03665/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.03665/record.json)

## Approaches

- [Alternative generation methods](https://latent-implicit-thinking.github.io/topics/alternative-reasoning-schedules/): 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.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.