Do LLMs Encode Functional Importance of Reasoning Tokens?

• URL: http://arxiv.org/abs/2601.03066v1
• Date: Tue, 06 Jan 2026 14:50:02 GMT
• Title: Do LLMs Encode Functional Importance of Reasoning Tokens?
• Authors: Janvijay Singh, Dilek Hakkani-Tür,
• Abstract summary: We propose greedy pruning, a likelihood-preserving deletion procedure that iteratively removes reasoning tokens.<n>We show that students trained on pruned chains outperform a frontier-model-supervised compression baseline at matched reasoning lengths.
• Score: 11.21558453188654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models solve complex tasks by generating long reasoning chains, achieving higher accuracy at the cost of increased computational cost and reduced ability to isolate functionally relevant reasoning. Prior work on compact reasoning shortens such chains through probabilistic sampling, heuristics, or supervision from frontier models, but offers limited insight into whether models internally encode token-level functional importance for answer generation. We address this gap diagnostically and propose greedy pruning, a likelihood-preserving deletion procedure that iteratively removes reasoning tokens whose removal minimally degrades model likelihood under a specified objective, yielding length-controlled reasoning chains. We evaluate pruned reasoning in a distillation framework and show that students trained on pruned chains outperform a frontier-model-supervised compression baseline at matched reasoning lengths. Finally, our analysis reveals systematic pruning patterns and shows that attention scores can predict greedy pruning ranks, further suggesting that models encode a nontrivial functional importance structure over reasoning tokens.

Related papers

• CoLT: Reasoning with Chain of Latent Tool Calls [31.228763375347608]
  Chain-of-Thought (CoT) is a critical technique in enhancing the reasoning ability of Large Language Models (LLMs)<n>We propose CoLT, a novel framework that implements latent reasoning as tool calls''
  arXiv  Detail & Related papers  (2026-02-04T06:12:53Z)
• Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models [29.494777688316674]
  Large Reasoning Models (LRMs) excel at solving complex problems by explicitly generating a reasoning trace before deriving the final answer.<n>However, extended generations incur substantial memory footprint and computational overhead, bottlenecking LRMs' efficiency.<n>This work uses attention maps to analyze the influence of reasoning traces and uncover an interesting phenomenon.<n>Only some decision-critical tokens in a reasoning trace steer the model toward the final answer, while the remaining tokens contribute negligibly.
  arXiv  Detail & Related papers  (2026-01-26T11:31:40Z)
• Reinforced Efficient Reasoning via Semantically Diverse Exploration [73.41112984160992]
  Reinforcement learning with verifiable rewards (RLVR) has proven effective in enhancing the reasoning of large language models (LLMs)<n>We propose reinforced efficient reasoning via semantically diverse explorations, i.e., ROSE, for LLMs.<n>Our method incorporates a semantic-entropy-based branching strategy and an $varepsilon$-exploration mechanism.
  arXiv  Detail & Related papers  (2026-01-08T15:56:44Z)
• In-Token Rationality Optimization: Towards Accurate and Concise LLM Reasoning via Self-Feedback [38.915062716409686]
  InTRO is a new framework that enables both token-level exploration and self-feedback for accurate and concise reasoning.<n>InTRO consistently outperforms other baselines, raising solution accuracy by up to 20% relative to the base model.<n>Its chains of thought are notably more concise, exhibiting reduced verbosity.
  arXiv  Detail & Related papers  (2025-11-13T01:47:06Z)
• Stop Spinning Wheels: Mitigating LLM Overthinking via Mining Patterns for Early Reasoning Exit [114.83867400179354]
  Overthinking can degrade overall performance of large language models.<n>We categorize reasoning into three stages: insufficient exploration stage, compensatory reasoning stage, and reasoning convergence stage.<n>We develop a lightweight thresholding strategy based on rules to improve reasoning accuracy.
  arXiv  Detail & Related papers  (2025-08-25T03:17:17Z)
• ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation [74.37307916314407]
  We propose a framework dubbed ConciseHint, which continuously encourages the reasoning model to speak concisely.<n>Experiments on the state-of-the-art LRMs, including DeepSeek-R1 and Qwen-3 series, demonstrate that our method can effectively produce concise reasoning.
  arXiv  Detail & Related papers  (2025-06-23T16:20:44Z)
• Think Clearly: Improving Reasoning via Redundant Token Pruning [57.01254508252785]
  We show that deliberately removing redundancy in the reasoning process significantly improves performance.<n>We demonstrate that our method significantly improves overall accuracy across reasoning-intensive benchmarks without any training.
  arXiv  Detail & Related papers  (2025-06-17T06:04:01Z)
• PixelThink: Towards Efficient Chain-of-Pixel Reasoning [70.32510083790069]
  PixelThink is a simple yet effective scheme that integrates externally estimated task difficulty and internally measured model uncertainty.<n>It learns to compress reasoning length in accordance with scene complexity and predictive confidence.<n> Experimental results demonstrate that the proposed approach improves both reasoning efficiency and overall segmentation performance.
  arXiv  Detail & Related papers  (2025-05-29T17:55:49Z)
• Fractured Chain-of-Thought Reasoning [61.647243580650446]
  We introduce Fractured Sampling, a unified inference-time strategy that interpolates between full CoT and solution-only sampling.<n>We show that Fractured Sampling consistently achieves superior accuracy-cost trade-offs, yielding steep log-linear scaling gains in Pass@k versus token budget.
  arXiv  Detail & Related papers  (2025-05-19T11:30:41Z)
• Process or Result? Manipulated Ending Tokens Can Mislead Reasoning LLMs to Ignore the Correct Reasoning Steps [39.759594479826454]
  We explore how vulnerable are reasoning models to subtle errors in their input reasoning chains.<n>We introduce "Compromising Thought" (CPT), a vulnerability where models presented with reasoning tokens containing manipulated calculation results tend to ignore correct reasoning steps and adopt incorrect results instead.<n>Our work enhances understanding of reasoning robustness and highlights security considerations for reasoning-intensive applications.
  arXiv  Detail & Related papers  (2025-03-25T03:43:11Z)
• Unveiling Reasoning Thresholds in Language Models: Scaling, Fine-Tuning, and Interpretability through Attention Maps [3.8936716676293917]
  This study investigates the in-context learning capabilities of various decoder-only transformer-based language models with different model sizes and training data.<n>We identify a critical parameter threshold (1.6 billion), beyond which reasoning performance improves significantly in tasks such as commonsense reasoning in multiple-choice question answering and deductive reasoning.
  arXiv  Detail & Related papers  (2025-02-21T00:48:32Z)
• Token-Supervised Value Models for Enhancing Mathematical Problem-Solving Capabilities of Large Language Models [56.32800938317095]
  Existing verifiers are sub-optimal for tree search techniques at test time.<n>We propose token-supervised value models (TVMs)<n>TVMs assign each token a probability that reflects the likelihood of reaching the correct final answer.
  arXiv  Detail & Related papers  (2024-07-12T13:16:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.