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arxiv: 2505.13975 · v4 · submitted 2025-05-20 · 💻 cs.CL

DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models

Yuxuan Jiang , Dawei Li , Francis Ferraro This is my paper

Pith reviewed 2026-05-22 14:06 UTC · model grok-4.3

classification 💻 cs.CL
keywords distilled reasoning pruningchain of thoughttoken efficiencylarge reasoning modelsmathematical reasoningdistillationpruningskill decomposition
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The pith

Distilled Reasoning Pruning lets student models solve math problems with far fewer tokens while matching or exceeding original accuracy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Distilled Reasoning Pruning to shorten the long reasoning traces that make large reasoning models inefficient. A teacher model first decomposes problems into skill-aware steps and prunes away excess content, then these concise paths are distilled into a student model so it learns to reason both accurately and briefly. Experiments across math datasets show clear efficiency gains. Readers care because current models waste tokens on verbose chains of thought, and a method that trims them without accuracy cost could make complex reasoning practical at scale.

Core claim

DRP uses a teacher model to perform skill-aware step decomposition and content pruning, then distills the resulting shorter reasoning paths into a student model. This hybrid of inference-time pruning and distillation produces models that reason efficiently and accurately. On GSM8K average token usage falls from 917 to 328 while accuracy rises from 91.7 percent to 94.1 percent; on AIME the method delivers a 43 percent token reduction with no performance drop. Further analysis indicates that aligning the structure of the training chains of thought with the student's reasoning capacity is essential for successful knowledge transfer.

What carries the argument

Skill-aware step decomposition performed by the teacher, which identifies reasoning steps according to required skills and prunes content to create concise paths suitable for distillation to the student.

Load-bearing premise

The teacher model's skill-aware decomposition produces pruned paths that match the student's own reasoning capacity and support effective knowledge transfer during distillation.

What would settle it

Training the same student on unpruned teacher traces versus DRP-pruned traces and finding no measurable gain in token efficiency or accuracy would show the pruning step adds no benefit.

Figures

Figures reproduced from arXiv: 2505.13975 by Dawei Li, Francis Ferraro, Yuxuan Jiang.

Figure 1
Figure 1. Figure 1: An overview of our proposed Distilled Reason [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of DRP framework. The student model generates Long-CoT reasoning traces, which are first [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Normalized token length distributions across [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The x-axis denotes the model’s maximum generation length, and the y-axis shows the proportion of correct answers recovered within that budget, with the R1-Distill-Qwen-7B. As shown in [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
read the original abstract

While Large Reasoning Models (LRMs) have demonstrated success in complex reasoning tasks through long chain-of-thought (CoT) reasoning, their inference often involves excessively verbose reasoning traces, resulting in substantial inefficiency. To address this, we propose Distilled Reasoning Pruning (DRP), a hybrid framework that combines inference-time pruning with tuning-based distillation, two widely used strategies for efficient reasoning. DRP uses a teacher model to perform skill-aware step decomposition and content pruning, and then distills the pruned reasoning paths into a student model, enabling it to reason both efficiently and accurately. Across several challenging mathematical reasoning datasets, we find that models trained with DRP achieve substantial improvements in token efficiency without sacrificing accuracy. Specifically, DRP reduces average token usage on GSM8K from 917 to 328 while improving accuracy from 91.7% to 94.1%, and achieves a 43% token reduction on AIME with no performance drop. Further analysis shows that aligning the reasoning structure of training CoTs with the student's reasoning capacity is critical for effective knowledge transfer and performance gains.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper proposes Distilled Reasoning Pruning (DRP), a hybrid framework that uses a teacher model to perform skill-aware step decomposition and content pruning on chain-of-thought traces, followed by distillation of the resulting shorter paths into a student model. The central empirical claim is that this alignment of reasoning structure with student capacity yields large gains in token efficiency on mathematical reasoning benchmarks without accuracy loss: average tokens on GSM8K drop from 917 to 328 while accuracy rises from 91.7% to 94.1%, and a 43% token reduction is achieved on AIME with no performance drop.

Significance. If the results are robust, DRP offers a practical route to more efficient inference in large reasoning models by combining pruning and distillation. The emphasis on matching pruned CoT structure to the student's capacity is a useful conceptual contribution, and the reported token reductions on standard benchmarks (GSM8K, AIME) are large enough to be practically relevant for deployment. The work ships concrete empirical measurements rather than self-referential quantities.

major comments (1)
  1. [§4 and Table 1] §4 (Experimental Setup) and Table 1: the headline token and accuracy numbers (917→328 tokens and 91.7%→94.1% on GSM8K; 43% reduction on AIME) are presented without reported variance across seeds, statistical significance tests, or an explicit description of the exact pruning criteria and baseline models used for comparison. These details are load-bearing for assessing whether the claimed efficiency gains are reproducible and attributable to the skill-aware decomposition rather than other factors.
minor comments (2)
  1. [§3.2] The abstract and §3.2 use the phrase 'skill-aware step decomposition' without a concise formal definition or pseudocode; adding a short algorithmic sketch would improve clarity for readers.
  2. [Figure 2] Figure 2 (or equivalent ablation plot) would benefit from error bars or multiple runs to visually support the claim that alignment with student capacity is critical.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and positive recommendation for minor revision. We agree that additional details on reproducibility are valuable and will incorporate them in the revised manuscript.

read point-by-point responses

  1. Referee: [§4 and Table 1] §4 (Experimental Setup) and Table 1: the headline token and accuracy numbers (917→328 tokens and 91.7%→94.1% on GSM8K; 43% reduction on AIME) are presented without reported variance across seeds, statistical significance tests, or an explicit description of the exact pruning criteria and baseline models used for comparison. These details are load-bearing for assessing whether the claimed efficiency gains are reproducible and attributable to the skill-aware decomposition rather than other factors.

    Authors: We agree that variance, significance testing, and explicit criteria strengthen the claims. In the revised manuscript we will add: (i) results averaged over three random seeds with standard deviations for token count and accuracy on both GSM8K and AIME; (ii) paired t-tests (or Wilcoxon tests) reporting p-values against the main baselines; and (iii) an expanded §4 that lists the precise skill-aware decomposition rules (step-level skill tags and pruning thresholds), content-pruning heuristics, and the exact baseline configurations (unpruned LRM, standard CoT distillation, and length-only pruning). These additions will make clear that the reported gains arise from capacity-aligned structure rather than other factors. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper proposes an empirical hybrid framework (DRP) combining teacher-driven skill-aware pruning with distillation, then evaluates the resulting student models on standard benchmarks (GSM8K, AIME). All reported outcomes—token reductions from 917 to 328 on GSM8K with accuracy rising from 91.7% to 94.1%, and 43% reduction on AIME—are direct measurements from training and inference runs, not quantities derived from equations or parameters that are defined in terms of the target results themselves. No self-definitional steps, fitted-input predictions, or load-bearing self-citations appear in the method or results chain; the central claims rest on external benchmark performance rather than internal redefinition or renaming of inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review is limited to the abstract; no explicit free parameters, axioms, or invented entities are described. The approach relies on standard distillation and pruning techniques from prior literature.

pith-pipeline@v0.9.0 · 5725 in / 969 out tokens · 43328 ms · 2026-05-22T14:06:16.372862+00:00 · methodology

discussion (0)

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation

    cs.CL 2026-05 unverdicted novelty 7.0

    Persistent 'Rock Tokens' in on-policy distillation resist teacher corrections, consume large gradient norms, yet add negligible value to reasoning, allowing targeted bypassing to streamline alignment.

  2. Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost

    cs.AI 2026-05 conditional novelty 7.0

    Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.

  3. MUSE: Resolving Manifold Misalignment in Visual Tokenization via Topological Orthogonality

    cs.CV 2026-05 unverdicted novelty 6.0

    MUSE decouples reconstruction and semantic learning in visual tokenization via topological orthogonality, yielding SOTA generation quality and improved semantic performance over its teacher model.

  4. ActorMind: Emulating Human Actor Reasoning for Speech Role-Playing

    cs.SD 2026-04 unverdicted novelty 5.0

    ActorMind is a four-agent chain-of-thought framework that emulates human actors to produce spontaneous, emotion-infused speech responses for role-playing scenarios.

  5. Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models

    cs.CL 2025-03 accept novelty 5.0

    A survey organizing techniques to achieve efficient reasoning in LLMs by shortening chain-of-thought outputs.

  6. Reinforcement Learning for Scalable and Trustworthy Intelligent Systems

    cs.LG 2026-05 unverdicted novelty 3.0

    Reinforcement learning is advanced for communication-efficient federated optimization and for preference-aligned, contextually safe policies in large language models.

Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · cited by 6 Pith papers · 3 internal anchors

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    Keep it unchanged

    KEEP: The step is necessary and already concise. Keep it unchanged

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    SINGLE-STEP COMPRESS: The step is necessary but verbose; rewrite it in a more concise way

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    The answer is

    MULTI-STEP COMPRESS: The step can be merged with neighboring steps; write a combined, cleaner version. If the final step clarifies the final answer (e.g., “The answer is...”), retain it. After completing the revision of each step, synthesize the revised steps into a coherent explanation. Ensure the output is fluent, log- ically sound, and matches the spea...

    work page 2023

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    SINGLE-STEP COMPRESS: Joy reads 8 pages in 20 minutes, so she reads 0.4 pages per minute (8 ÷ 20)

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    SINGLE-STEP COMPRESS: To find the time for 120 pages, divide 120 by 0.4 pages per minute

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    DELETE: This step is now included in the previous step

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    SINGLE-STEP COMPRESS: Convert 300 minutes to hours by dividing by 60

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    Final Answer: Okay, let’s calculate the time it takes Joy to read 120 pages

    DELETE: This step is now included in the previous step. Final Answer: Okay, let’s calculate the time it takes Joy to read 120 pages. Joy reads 8 pages in 20 minutes, which means she reads 0.4 pages per minute (8 ÷ 20). To find out how many minutes it takes her to read 120 pages, divide 120 by 0.4, resulting in 300 minutes. Convert 300 minutes into hours b...

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