# Large Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers

Authors: Ziyi Chen, Yan Zhang, Jianhui Wei, Jianing Yin, Daoan Zhang.

Trains a language model to score labels supplied at inference in one forward pass, without generating a reasoning trace. Distills label distributions from repeated teacher reasoning and studies routing uncertain decisions back to explicit reasoning.

First public: 2026-10-11.
ArXiv initial submission: 2026-10-11.

[Original paper](https://arxiv.org/abs/2610.15501) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2610.15501/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2610.15501/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2610.15501/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2610.15501/record.json)

## Approaches

- [Internalized reasoning](https://latent-implicit-thinking.github.io/topics/internalized-reasoning/): Learn from worked solutions so that some or all of the reasoning can later happen without writing those steps. Training may use a separate teacher model or progressively remove written steps from the model being trained.

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.