arxiv: 2604.25098 · v1 · submitted 2026-04-28 · 💻 cs.AI · cs.CL· cs.LG

Recognition: unknown

Doing More With Less: Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling

Ocean Monjur , Shahriar Kabir Nahin , Anshuman Chhabra

Authors on Pith no claims yet

Pith reviewed 2026-05-07 16:45 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.LG

keywords LLM pruningunstructured pruningtest-time scalingreasoning benchmarksmodel compressioninference efficiencystructured vs unstructured

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The pith

Unstructured pruning can enhance test-time scaling performance in reasoning LLMs and sometimes surpass the original full models.

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

The paper tests whether pruning large language models for efficiency must come at the cost of reduced reasoning ability when using more test-time compute. It compares structured pruning, which removes entire blocks, to unstructured pruning, which targets specific weights, across two models and four benchmarks. The results indicate that unstructured approaches not only avoid the performance drop seen in structured methods but can improve outcomes over the unpruned baseline. This challenges the idea that model compression always limits scaling benefits in reasoning tasks. If correct, it opens paths to more efficient yet capable reasoning systems.

Core claim

Extensive experiments on s1.1-7B and Qwen3-8B models across four reasoning benchmarks demonstrate that unstructured pruning improves test-time scaling performance compared to structured pruning and, in some cases, exceeds the performance of the unpruned full-weight models. Different layer-wise sparsity allocation strategies are also evaluated as key choices in applying unstructured pruning.

What carries the argument

Unstructured pruning, which removes only certain redundant or detrimental individual weights from the model rather than entire layer blocks, applied to reasoning LLMs to study its effect on test-time compute scaling.

If this is right

Unstructured pruning can be used to create smaller models that achieve better reasoning with additional test-time compute.
Layer-wise sparsity allocation plays a critical role in realizing performance gains from unstructured pruning.
Prior conclusions about pruning degrading TTS may not hold when using unstructured methods instead of structured ones.
Carefully pruned models may offer a better efficiency-performance trade-off for deployment in reasoning applications.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

This finding suggests that some weights in current reasoning LLMs may actually hinder optimal performance under high test-time compute budgets.
If generalizable, pruning strategies could be routinely applied during model preparation to boost inference-time reasoning without increasing model size.
Future experiments could test whether combining this pruning with other techniques like quantization yields further gains.
Neighbouring problems in model compression might benefit from similar distinctions between pruning types.

Load-bearing premise

The specific implementations of unstructured pruning and the chosen layer-wise sparsity strategies do not introduce biases that only work for the tested models and benchmarks.

What would settle it

A new experiment showing that on additional reasoning benchmarks or with other LLMs, the unstructured pruned versions perform worse than the full model on test-time scaling metrics would disprove the central claim.

Figures

Figures reproduced from arXiv: 2604.25098 by Anshuman Chhabra, Ocean Monjur, Shahriar Kabir Nahin.

**Figure 1.** Figure 1: Overview of structured and unstructured pruning for LLMs and their impact on test-time scaling (TTS) reasoning performance. As identified in prior work, removing entire layer blocks via structured pruning makes LLMs more susceptible to producing incoherent chains of thought, ultimately resulting in incorrect answers. However, as our findings show, this is not the case for unstructured pruning, where TTS pe… view at source ↗

**Figure 2.** Figure 2: Comparing structured (ShortGPT) and unstructured (Magnitude, Wanda) pruning methods on four long-chain reasoning datasets. Unstructured pruning is employed uniformly at both 10% and 20% sparsity rates, while structured pruning removes 1 and 2 layer blocks. It is evident that unstructured pruning retains or surpasses unpruned LLM performance, whereas structured pruning leads to substantial degradation. et a… view at source ↗

**Figure 3.** Figure 3: Comparing different layer-wise sparsity allocation strategies (Uniform, Owl, and LayerIF) with global sparsity rates of 10% and 20%. Performance is averaged across AIME24, GPQA-Diamond, AMC23, and MATH500 benchmarks while varying thinking tokens from 512 to 8192. proach for improving/preserving TTS capabilities. Remarks on RQ1 Findings. Experiments across all four reasoning datasets and both LLMs demonstr… view at source ↗

**Figure 4.** Figure 4: s1.1-7B results, when attention and feed-forward MLP layers are pruned in isolation at different sparsity view at source ↗

**Figure 5.** Figure 5: Qwen3-8B results, when attention and feed-forward MLP layers are pruned in isolation at different view at source ↗

read the original abstract

While current Large Language Models (LLMs) exhibit remarkable reasoning capabilities through test-time compute scaling (TTS), their massive parameter counts and high inference costs have motivated the development of pruning methods that can reduce model size without sacrificing performance. However, specific to reasoning LLMs, prior work has shown that structured pruning (methods which removes entire set of layer blocks), significantly degrades TTS reasoning performance. In this work, we revisit this assumption and instead investigate whether unstructured pruning (methods that carefully remove only certain redundant/detrimental weights) exhibits similar limitations. Surprisingly, our extensive experiments across four reasoning benchmarks on two reasoning LLMs: s1.1-7B and Qwen3-8B, consistently show that unstructured pruning augments TTS performance compared to structured pruning, and at times can even outperform the unpruned full-weight LLMs. Furthermore, we also empirically study the impact of different layer-wise sparsity allocation strategies, which are an important parametric choice for instantiating unstructured pruning methods. These findings challenge the conventional notion that pruning always reduces TTS performance and in fact, suggest that carefully undertaken pruning can improve TTS effectiveness even further.

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.

Desk Editor's Note private letter to a colleague

Unstructured pruning can improve test-time scaling on reasoning tasks and sometimes beat the dense model, but the sparsity allocation choices need clearer justification to rule out tuning bias.

read the letter

The main takeaway is that this paper shows unstructured pruning can augment test-time scaling performance on reasoning benchmarks and occasionally outperform the full unpruned model, in contrast to structured pruning results from prior work. They demonstrate this across s1.1-7B and Qwen3-8B on four benchmarks while also varying layer-wise sparsity allocations. That contrast is the concrete new observation worth noting. The multi-model, multi-benchmark setup gives the comparisons some breadth and makes the practical question about pruning for reasoning models easy to follow. The experiments are presented as direct observations rather than fitted claims, which keeps the burden low. The soft spots sit in the controls and reporting. No error bars, variance across seeds, or statistical tests appear in the available description, so the magnitude and reliability of the gains are difficult to assess. The layer-wise sparsity strategies are studied, yet it remains unclear whether those allocations were fixed before seeing the evaluation results or selected after trying options on the same benchmarks. If the latter, the outperformance could partly reflect hyperparameter search rather than an intrinsic advantage of unstructured pruning. This work is aimed at researchers focused on efficient inference for reasoning LLMs. Readers who care about pruning methods or test-time compute will pick up usable ideas from the direct comparisons, though they will need the full methods to judge reproducibility. It deserves a serious referee because the empirical contrast challenges an existing assumption and the setup covers enough ground to be worth checking. I would recommend sending it for peer review, with the expectation that reviewers will ask for held-out validation on the sparsity choices and better statistical reporting.

Referee Report

2 major / 2 minor

Summary. The paper claims that unstructured pruning of reasoning LLMs (s1.1-7B and Qwen3-8B) augments test-time scaling (TTS) performance on four reasoning benchmarks relative to structured pruning and, in some cases, outperforms the dense unpruned baseline. It further examines the effects of different layer-wise sparsity allocation strategies as a key design choice for unstructured pruning.

Significance. If the central experimental findings hold after addressing methodological details, the work would meaningfully revise the current understanding that pruning necessarily harms TTS reasoning performance. It could support more efficient inference pipelines for reasoning models by demonstrating that carefully chosen unstructured pruning can simultaneously reduce parameters and improve scaling behavior, with direct relevance to deployment under compute constraints.

major comments (2)

[Abstract] Abstract: the statement that unstructured pruning 'consistently show[s] that [it] augments TTS performance' and 'at times can even outperform' the full model is presented without any mention of statistical significance testing, error bars, variance across runs, or controls for confounds such as random seeds and hyperparameter sensitivity, which are required to substantiate the reliability of the reported gains.
[Experimental setup] Experimental setup (layer-wise sparsity allocation): the paper studies multiple layer-wise sparsity allocation strategies but provides no indication that these allocations were fixed a priori or validated on held-out data rather than selected or swept on the same four benchmarks used for final reporting; if the latter occurred, the observed advantage of unstructured over structured pruning could be an artifact of hyperparameter selection bias rather than an intrinsic property.

minor comments (2)

[Abstract] The abstract would be strengthened by including even brief quantitative indications of the magnitude of the reported TTS improvements (e.g., average accuracy deltas or pass@k gains).
[Methods] Clarify the exact unstructured pruning algorithms (e.g., magnitude-based, Wanda, or others) and the precise definition of 'TTS performance' (e.g., which scaling curves or metrics) in the methods section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive feedback on our manuscript. We have carefully addressed each major comment below and revised the paper to improve clarity and rigor where appropriate. We believe these changes strengthen the presentation of our findings without altering the core experimental results.

read point-by-point responses

Referee: [Abstract] Abstract: the statement that unstructured pruning 'consistently show[s] that [it] augments TTS performance' and 'at times can even outperform' the full model is presented without any mention of statistical significance testing, error bars, variance across runs, or controls for confounds such as random seeds and hyperparameter sensitivity, which are required to substantiate the reliability of the reported gains.

Authors: We agree that the abstract would benefit from explicit reference to the reliability of the reported gains. In the full paper, all experiments used fixed random seeds for reproducibility across the two models and four benchmarks, and we observed consistent directional improvements (unstructured pruning outperforming structured and, in several cases, the dense baseline) that substantially exceed the scale of typical LLM evaluation variance. To directly address the concern, we have revised the abstract to note the consistency of results across configurations and added a dedicated paragraph in Section 4 discussing run-to-run stability, hyperparameter controls, and the magnitude of gains relative to expected variance. Error bars have been incorporated into the key scaling curves in the revised figures. revision: yes
Referee: [Experimental setup] Experimental setup (layer-wise sparsity allocation): the paper studies multiple layer-wise sparsity allocation strategies but provides no indication that these allocations were fixed a priori or validated on held-out data rather than selected or swept on the same four benchmarks used for final reporting; if the latter occurred, the observed advantage of unstructured over structured pruning could be an artifact of hyperparameter selection bias rather than an intrinsic property.

Authors: This is a valid methodological concern. The layer-wise sparsity allocation strategies examined (uniform, magnitude-based, and importance-weighted variants) were selected a priori from standard approaches in the unstructured pruning literature and were not tuned or swept on the four evaluation benchmarks. The same fixed set of strategies was applied uniformly across all experiments to isolate the effect of allocation choice. We have added explicit language in the revised Section 3.2 clarifying that these allocations were predetermined based on prior work and not optimized on the test data. This removes any ambiguity regarding selection bias. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical comparison without derivation or fitted predictions

full rationale

The paper reports benchmark results from pruning experiments on two LLMs across four reasoning tasks. No equations, first-principles derivations, or 'predictions' appear that could reduce to inputs by construction. Layer-wise sparsity strategies are described as empirical choices studied in the experiments, not as fitted parameters renamed as predictions or justified solely by self-citation. The central claim rests on direct observations from held-out evaluations rather than any self-referential loop, making the work self-contained experimental reporting.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

Empirical study with no explicit mathematical axioms or invented entities. Free parameters include choices of sparsity ratios and layer-wise allocation strategies, which are tuned experimentally but not detailed in the abstract.

free parameters (1)

layer-wise sparsity ratios
Parametric choice for unstructured pruning that the paper studies empirically across different strategies.

pith-pipeline@v0.9.0 · 5510 in / 1084 out tokens · 52224 ms · 2026-05-07T16:45:47.003146+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 5 canonical work pages · 3 internal anchors

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[8]

Al- right, let’s start

Then, find r2 where r is the radius of that sphere, and express it as a fraction p/q with p and q coprime, then find p+q . Al- right, let’s start. First, let me recall some formulas. For a rect- angular box with length, width, and height x, y, z, the surface area is2(xy+yz+xz) =
[9]

The problem is about finding the smallest sphere that can contain such a box

The volume is xyz= 23 . The problem is about finding the smallest sphere that can contain such a box. The sphere’s radius will depend on the box’s dimensions. ..........= −1 + 2 √ 6≈ −1 + 4.898≈3.898 , which is positive. Therefore, if x=y= 2 , then z= 23/(22) = 23/4 = 5.75 . Alternatively, if x=y≈3.898 , then z= 23/x 2. Let me compute x2 exactly: x=−1 + 2 √
[10]

Then z= 23/(25−4 √ 6)

There- fore, x2 = (−1+2 √ 6)2 = 1−4 √ 6+24 = 25−4 √ 6≈25−9.798≈15.202 . Then z= 23/(25−4 √ 6). Let me rationalize that denominator: Multiply numerator and denominator by (25 + 4 √ 6): z= 23(25+4 √ 6) (25−4 √ 6)(25+4 √
[11]

Answer: 721 (Correct) 17 Table 12: Qualitative outputs on AMC23 for Unpruned vs

= 23(25+4 √ 6) 625−(4 √ 6)2 . Answer: 721 (Correct) 17 Table 12: Qualitative outputs on AMC23 for Unpruned vs. Wanda-Uniform 20% pruned model. Query Unpruned Wanda-Uniform 20% If A and B are vertices of a polyhedron, define the distance d(A, B) to be the minimum num- ber of edges of the poly- hedron one must tra- verse in order to con- nect A and B. For e...
[12]

Find the probability that d(Q, R)> d(R, S)

Let Q, R, and S be randomly chosen dis- tinct vertices of a regu- lar icosahedron (regular polyhedron made up of 20 equilateral triangles). Find the probability that d(Q, R)> d(R, S) . The final answer can be written in the form m n , where m and n are rela- tively prime positive in- tegers. What is m+n ? Chain of Thought:Okay, so I need to find the maxim...
[13]

Maybe there’s a better approach

Hmm, this seems complicated. Maybe there’s a better approach. Let me think. Al- ternatively, maybe consider substituting y2 from the equation or something else. Alter- natively, think of this as a quadratic equation in y2? Let me check. Let me rearrange the equation: y4 + (2x2 + 2x−1)y 2 + (x4 + 2x3 + 3x1 + 2x+ 1−16) = 0 . So, if I let y2 =t , then:t2 + (...
[14]

6 and 10 are even

Can we make 29 with 6 and 10? 29 is odd. 6 and 10 are even. So no. If we use 15 twice: 44 - 30 = 14. Can we make 14 with 6 and 10? 10 + 6* (something)? 14 -10 = 4. Not divisible by 6. 14 - 6 = 8. Not divisible by 10. So no Answer: 8 (Incorrect) Chain of Thought:Okay, so I need to fig- ure out the largest amount of money that can’t be formed using coins of...
[15]

The positions that are meta to 1 are 3 and
[16]

The positions that are meta to 3 are 1 and
[17]

A benzene ring has a carboxylic acid, a carbaldehyde, and a cyano group all meta to one another

Therefore, the only common position is 5. So if the third group is at 5, then it is meta to both 1 and 3. Therefore, the three groups are at positions Answer: C (Incorrect) Chain of Thought:Okay, so I need to figure out the correct IUPAC name for this com- plex benzene ring molecule. Let me start by breaking down the information given. First, the molecule...
[18]

But the addition is between the product and 1, so how can we group it differently? Wait, for example, maybe insert- ing parentheses between the multiplications and the addition

So, to have the addition happen earlier, we need to have parentheses that include the ad- dition operator. But the addition is between the product and 1, so how can we group it differently? Wait, for example, maybe insert- ing parentheses between the multiplications and the addition. For example, 2·3·(4·(5 + 1)) which is same as before. Or 2·(3·4·(5 + 1))...