# Hybrid Latent Reasoning via Reinforcement Learning

Authors: Zhenrui Yue, Bowen Jin, Huimin Zeng, Honglei Zhuang, Zhen Qin, Jinsung Yoon, Lanyu Shang, Jiawei Han, Dong Wang.

Uses reinforcement learning to choose and train a mixture of latent and written reasoning steps.

First public: 2025-05-24.
ArXiv initial submission: 2025-05-24.

[Original paper](https://arxiv.org/abs/2505.18454) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2505.18454/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2505.18454/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2505.18454/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2505.18454/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.
- [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.