---
title: "Think Less, Know More — paper by Sui et al. (2026)"
description: "The authors present STACK, which compresses reasoning step by step and decides how to compress based on the model's… 53 references."
authors: ["Yi Sui", "Chaozhuo Li", "Dawei Song"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1128/"
doi: "10.18653/v1/2026.findings-acl.1128"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sui-2026-think-less-know-more-state/"
---
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# Think Less, Know More: State-Aware Reasoning Compression with Knowledge Guidance for Efficient Reasoning

**Authors:** Yi Sui, [Chaozhuo Li](https://md.research.elamur.ai/free/en/authors/chaozhuo-li/) and [Dawei Song](https://md.research.elamur.ai/free/en/authors/dawei-song/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1128/) · [PDF](https://aclanthology.org/2026.findings-acl.1128.pdf) · [DOI 10.18653/v1/2026.findings-acl.1128](https://doi.org/10.18653/v1/2026.findings-acl.1128) · [OpenAlex](https://openalex.org/W7166860003)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

Large Reasoning Models tend to "overthink." They produce very long Chain-of-Thought (CoT) traces regardless of how hard a problem is, which raises latency and compute cost. Earlier compression methods usually tune brevity per task and often lose accuracy. The authors present STACK, which compresses reasoning step by step and decides how to compress based on the model's current state.

STACK tracks local token entropy against a sliding-window baseline to spot "hesitation" steps where the model is uncertain:
- **Uncertain steps:** retrieved external evidence is injected through Knowledge-Guided Contrastive Decoding (KGCD).
- **Confident but verbose stretches:** the model is prompted to trim its own redundant steps.
- **Late re-verification:** an early-stopping rule halts reasoning once the answer distribution converges.

The compressed chains are paired online with the original long chains as contrastive samples. Training uses a DPO objective augmented with PPO-style reward differences. On three math benchmarks, STACK cuts average response length by 59.9% and raises accuracy by 4.8 points over prior methods. This suggests that reasoning efficiency and reliability can be improved together.

## Key points

- **Hesitation detection:** entropy-based detection triggers retrieval only when needed. Always-on retrieval (fusion weight λ_w ≈ 0.98) gave lower accuracy (92.9 on MATH500, 56.3 on AIME24) than gated STACK (93.5 / 57.4).
- **Robustness to noisy retrieval:** with random top-1 replacement, accuracy dropped only to 92.4 on MATH500 and 55.4 on AIME24, while λ_w fell to 0.11. This indicates that unreliable evidence is down-weighted automatically.
- **Early stopping:** it affects efficiency rather than correctness. Removing it barely changes accuracy (93.3 / 57.1) but lengthens outputs to 2475 tokens on MATH500 and 9130 on AIME24.
- **Training ablation:** SFT followed by reward-difference-augmented DPO ("MDPO") balances accuracy and length better than SFT alone or SFT+DPO.
- **Hyperparameters and limitations:** the best settings across model scales were µ = 0.25 for the contrastive decoding coefficient and α = 0.5 for the compression reward weight. The listed limitations are higher training cost, imperfect retrieval quality, and reliance on text-only knowledge.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Beijing University of Posts and Telecommunications](https://md.research.elamur.ai/free/en/universities/beijing-university-of-posts-and-telecommunications/) — University, Beijing, China · 57 works · 32,843 citations
- [Beijing Institute of Technology](https://md.research.elamur.ai/free/en/universities/beijing-institute-of-technology/) — University, Beijing, China · 34 works · 3,652 citations
- [The Open University](https://md.research.elamur.ai/free/en/universities/the-open-university/) — University, Milton Keynes, United Kingdom · 10 works · 338 citations

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🧭 Topics (2): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works · [🪶 Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/) · 242 works

## Cite

Cite as: Yi Sui, Chaozhuo Li and Dawei Song. 2026. Think Less, Know More: State-Aware Reasoning Compression with Knowledge Guidance for Efficient Reasoning. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{sui2026think,
  title = {{Think Less, Know More: State-Aware Reasoning Compression with Knowledge Guidance for Efficient Reasoning}},
  author = {Yi Sui, Chaozhuo Li and Dawei Song},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1128},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1128}
}
```

- [← previous Think Hard Only When Needed: A Hybrid Best-of-N and Beam Search for Efficient Test-Time Compute](https://md.research.elamur.ai/free/en/papers/suh-2026-think-hard-only-when-needed/)
- [next → Think Like You Execute: Verifiable Chain of Thought from Program Traces](https://md.research.elamur.ai/free/en/papers/thakur-2026-think-like-you-execute-verifiable/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/45/)

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*Updated 1 November 2026*