# Looped Actor: Depth-Recurrent Reasoning Models for Reinforcement Learning

Authors: T. Konstantin Rusch, Tim Seyde, Jared Boyer, Zach J. Patterson, Daniela Rus.

Uses a shared transformer block to refine a reinforcement-learning policy's internal state before choosing an action. Learns to vary the number of loops with the current state, and studies adaptive computation in puzzles and robotic-control tasks.

First public: 2026-09-29.
ArXiv initial submission: 2026-09-29.

[Original paper](https://arxiv.org/abs/2609.37432) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.37432/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.37432/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.37432/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.37432/record.json)

## 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.