# Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient in Latent Space

Authors: Hengli Li, Chenxi Li, Tong Wu, Xuekai Zhu, Yuxuan Wang, Zhaoxin Yu, Eric Hanchen Jiang, Song-Chun Zhu, Zixia Jia, Ying Nian Wu, Zilong Zheng.

Optimizes latent states for individual problems at test time using policy gradients rather than searching over written reasoning traces.

First public: 2025-05-19.
ArXiv initial submission: 2025-05-19.

[Original paper](https://arxiv.org/abs/2505.13308) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2505.13308/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2505.13308/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2505.13308/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2505.13308/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.