# DART: Distributional Adversarial Recurrent Training for Algorithm Learning

Authors: Hieu Tran Bao, Phung Thanh Dang, Pham Quang Nhat Minh, Hoang Thanh Tung.

Trains recurrent reasoners against a distribution of targets near each correct solution, using an adversarial objective instead of a single point target. Evaluates generalization and stability on maze, chess, and Sudoku tasks.

First public: 2026-09-05.
ArXiv initial submission: 2026-09-05.

[Original paper](https://arxiv.org/abs/2609.05988) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.05988/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.05988/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.05988/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.05988/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.