# FlexLoop: Depth-Elastic Looped Policies for Adaptive Test-Time Computation in Deep RL

Authors: Xun Wang, Ruishuo Chen, Yu Chen, Zhuoran Li, Longbo Huang.

Post-trains a looped reinforcement-learning policy to make useful decisions at several recurrent depths. Combines its original objective with distillation between neighboring depths, then uses agreement across depths to decide when to stop.

First public: 2026-09-28.
ArXiv initial submission: 2026-09-28.

[Original paper](https://arxiv.org/abs/2609.34488) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.34488/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.34488/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.34488/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.34488/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.