Skip to main content

Beyond the Training Horizon: Mechanisms and Limits of Length Generalization in Looped Transformers

Jia Liang, Xi Jin, Liangming Pan

Compares how two looped-transformer training schemes solve multi-step tasks beyond their training length. Uses hidden-state interventions to identify how intermediate information moves through each model and where that process breaks down.

First public: · Added to this list: October 18, 2026

Cite this paper

ArXiv preprint

arXiv:2609.33144 · Initial submission: 2026-09-27

BibTeX · CSL-JSON

A published-version citation has not been verified for this entry. This does not mean the paper is unpublished.

Choose the version you used. The arXiv link may serve a revision; check the original paper for the exact result, assumptions and limitations you cite.

Explore this approach

Analysis and 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.

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.

Read or reuse this entry

Plain-text Markdown · JSON record · Find in the full list

Our summary and categories are a reading aid, not the authors’ abstract. Use the original paper for research claims. Suggest a correction.