# BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion

Authors: Ruurd J. A. Kuiper, Robert A. Bagheri.

Converts autoregressive instruction models into diffusion language models by enabling bidirectional attention and training query/value adapters on masked responses. Keeps the backbone and output head fixed, then fills several answer positions per decoding step.

First public: 2026-10-30.
ArXiv initial submission: 2026-10-30.

[Original paper](https://arxiv.org/abs/2610.41574) · [PDF](https://arxiv.org/pdf/2610.41574) · [HTML](https://arxiv.org/html/2610.41574v1) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.41574/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.41574/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.41574/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.41574/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 0.12.0; literature checked through 2026-11-02.