# Think-at-Hard: Dynamic Looped Transformers for Improved Reasoning

Authors: Tianyu Fu, Yichen You, Zekai Chen, Guohao Dai, Huazhong Yang, Yu Wang.

Uses dynamic looping to vary internal depth with problem difficulty and studies the resulting reasoning and efficiency trade-offs.

First public: 2025-11-11.
ArXiv initial submission: 2025-11-11.

[Original paper](https://arxiv.org/abs/2511.08577) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2511.08577/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2511.08577/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2511.08577/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2511.08577/record.json)
[Code](https://github.com/thu-nics/TaH)

## Approaches

- [Recurrent reasoning](https://latent-implicit-thinking.github.io/topics/recurrent-reasoning/): Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.

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.