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Do LLMs Encode Functional Importance of Reasoning Tokens ?

Paper · 2026 · Janvijay Singh and Dilek Hakkani-Tür

Summary

The paper asks whether an LLM internally "knows" which tokens in its own reasoning chain actually matter for reaching the answer. The authors treat compression as a diagnostic problem rather than an engineering one. They propose greedy pruning, which works like greedy decoding run in reverse. Starting from a full teacher-generated chain, it repeatedly deletes the single token whose removal causes the smallest drop in a pruner model's likelihood. This yields a global token ranking and chains of any chosen length. Two objectives are compared: keeping the answer likely, and keeping both the pruned reasoning and the answer likely. Pruned chains are tested in a teacher–pruner–student distillation setup on GSM8K, MATH and MMLU-Pro. Students trained on them beat several baselines at equal reasoning length, including TokenSkip, which relies on frontier-model importance labels. Further analysis shows that symbolic computation survives longest while connective wording goes first, and that attention signals predict pruning ranks well. This suggests models carry an accessible token-importance structure, which could support cheaper and more controllable reasoning.

Key points

  • Method: Greedy pruning is a likelihood-preserving, iterative deletion procedure with a keep fraction ρ.
    • It re-scores tokens as the context shrinks, so it captures interactions between tokens, unlike leave-one-out attribution.
    • It uses two objectives: answer-only log P(A | Q, R_K) and joint log P(R_K, A | Q).
  • Evaluation: Students distilled on greedily pruned chains outperform pruning baselines such as TokenSkip at matched lengths on GSM8K, MATH and MMLU-Pro.
  • Attention as a signal: With Llama3.1-8B as teacher, a surrogate built on attention-derived signals predicts the actual likelihoods after a token is removed, correlating with them at a Pearson value of 0.88 on held-out data.
  • What drives pruning quality: Results depend on the chosen objective and on the strength of the pruner model, with stronger pruners giving more reliable estimates.
  • Limitations:
    • Cost grows superlinearly with sequence length, so experiments use chains of about ≤500 tokens.
    • Only correctness-filtered traces from moderately sized instruction-tuned models are studied.
  • Code: github.com/iamjanvijay/greedy-token-pruner.

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

🎓University of Illinois Urbana-ChampaignUrbana · United States184 works · 69,372 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works

Cite

Cite as Janvijay Singh and Dilek Hakkani-Tür. 2026. Do LLMs Encode Functional Importance of Reasoning Tokens ?. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1419

@inproceedings{singh2026do,
  title = {{Do LLMs Encode Functional Importance of Reasoning Tokens ?}},
  author = {Janvijay Singh and Dilek Hakkani-Tür},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1419},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1419}
}

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