# Beyond Imitation: Reinforcement Learning for Active Latent Planning

Authors: Zhi Zheng, Wee Sun Lee.

Uses reinforcement learning to train an active latent-planning process rather than only imitating supervised reasoning traces.

First public: 2026-01-29.
ArXiv initial submission: 2026-01-29.

[Original paper](https://arxiv.org/abs/2601.21598) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2601.21598/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2601.21598/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2601.21598/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2601.21598/record.json)
[Code](https://github.com/zz1358m/ATP-Latent-master)

## 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.