# Chain Of Thought Compression: A Theoretical Analysis

Authors: Juncai Li, Ru Li, Yuxiang Zhou, Boxiang Ma, Jeff Z. Pan.

Analyzes how skipping intermediate steps weakens learning signals in a simplified parity-task model. Introduces NatBool-DAG to test logical dependencies and ALiCoT, which supervises learned token states with intermediate logical labels or explicit-CoT representations.

First public: 2026-01-29.
ArXiv initial submission: 2026-01-29.

[Original paper](https://arxiv.org/abs/2601.21576) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2601.21576/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2601.21576/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2601.21576/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2601.21576/record.json)

## Approaches

- [Internalized reasoning](https://latent-implicit-thinking.github.io/topics/internalized-reasoning/): Learn from worked solutions so that some or all of the reasoning can later happen without writing those steps. Training may use a separate teacher model or progressively remove written steps from the model being trained.
- [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.