# DELA: Learning Controllable Edits for Diffusion Language Models

Authors: Xinsong Feng, Chenan Wang, Leshu Tang, Johnny Liu, Dong Li, Da Li, Haipeng Chen.

Trains a small controller to insert, delete or remask parts of a text draft, while a frozen diffusion language model fills the gaps. Tests whether these targeted edits improve mathematical answers and code; intermediate reasoning remains explicit text.

First public: 2026-10-23.
ArXiv initial submission: 2026-10-23.

[Original paper](https://arxiv.org/abs/2610.32096) · [PDF](https://arxiv.org/pdf/2610.32096) · [HTML](https://arxiv.org/html/2610.32096v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.32096/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.32096/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.32096/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.32096/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. Some produce written chains of thought.

Reading-list version 0.11.0; literature checked through 2026-10-26.