# Learn Your Own Thoughts: Abstract Token Curriculum

Authors: Khashayar Gatmiry, Avrajit Ghosh, Parsa Mirtaheri, Jason D. Lee, Nika Haghtalab, Emmanuel Abbe, Peter Bartlett.

Trains on progressively harder problems so that a model learns continuous intermediate thoughts without explicit reasoning-step supervision. Analyzes parity learning theoretically and tests graph reachability and arithmetic.

First public: 2026-09-17.
ArXiv initial submission: 2026-09-17.

[Original paper](https://arxiv.org/abs/2609.19717) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.19717/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.19717/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.19717/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.19717/record.json)

## Approaches

- [Continuous-state reasoning](https://latent-implicit-thinking.github.io/topics/continuous-states/): Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.

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.