# Language Models Can Learn Implicit Multi-Hop Reasoning, but Only If They Have Lots of Training Data

Authors: Yuekun Yao, Yupei Du, Dawei Zhu, Michael Hahn, Alexander Koller.

Studies the data requirements for learning implicit multi-hop reasoning and identifies sharp limitations in low-data regimes.

First public: 2025-05-23.
ArXiv initial submission: 2025-05-23.

[Original paper](https://arxiv.org/abs/2505.17923) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2505.17923/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2505.17923/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2505.17923/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2505.17923/record.json)

## Approaches

- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

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.