# SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought

Authors: Jianwei Wang, Ziming Wu, Fuming Lai, Shaobing Lian, Ziqian Zeng.

Uses synthetic continuous reasoning trajectories to train language models to allocate internal reasoning adaptively.

First public: 2025-08-01.
ArXiv initial submission: 2025-08-01.

[Original paper](https://arxiv.org/abs/2508.00574) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2508.00574/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2508.00574/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2508.00574/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2508.00574/record.json)

## Approaches

- [Continuous-state reasoning](https://latent-implicit-thinking.github.io/topics/continuous-states/): Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.

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.