# Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective

Authors: Jaehyeon Kim, Suhwan Kim, Nakyung Lee, Yeongoon Kim, Jimin Seo, Giho Lee, Jungwoo Lee.

Places pause tokens at reasoning-step boundaries and masks their training loss. Studies how this affects learning new tasks while retaining earlier capabilities, including during reinforcement learning.

First public: 2026-09-03.
ArXiv initial submission: 2026-09-03.

[Original paper](https://arxiv.org/abs/2609.04489) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.04489/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.04489/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.04489/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.04489/record.json)

## Approaches

- [Thinking tokens](https://latent-implicit-thinking.github.io/topics/thinking-tokens/): Give a model extra tokens for computation, such as pauses, filler symbols, or learned planning tokens. These tokens need not spell out the solution.
- [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.