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arxiv: 2606.01544 · v1 · pith:AUID53T4new · submitted 2026-06-01 · 💻 cs.LG

CRePE: Convolution-aware Relative Importance in Post-training Pruning with Efficient Search

Cheonjun Park This is my paper

Pith reviewed 2026-06-28 15:41 UTC · model grok-4.3

classification 💻 cs.LG
keywords post-training pruninglarge language modelsrelative importancemodel compressionhyperparameter optimizationLLM efficiencypruning methods
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The pith

CRePE improves post-training pruning accuracy by adding 2D local neighborhood context and adaptive coefficients to relative importance scores.

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

Post-training pruning reduces LLM memory and compute costs by removing weights after training. RIA scored weight importance using only 1D row and column sums with equal weighting. CRePE extends this scoring to incorporate surrounding 2D neighborhood values and learned adaptive weights for the row versus column directions. The resulting pruned models retain higher accuracy across tested LLMs and sparsity levels. A separate proxy-based optimizer finds the adaptive weights in 20 minutes instead of 11 hours and the values transfer across models.

Core claim

CRePE extends relative importance scoring by incorporating 2D local neighborhood context around each weight and adaptive coefficients that balance row and column contributions, producing higher accuracy than prior post-training pruning methods on diverse models and sparsity settings. PHO replaces repeated full perplexity evaluations with proxy measurements to locate good coefficient values rapidly, and the discovered coefficients generalize to other models without retuning. The method combines orthogonally with channel permutation, non-uniform sparsity allocation, and re-pruning.

What carries the argument

Convolution-aware relative importance scoring that augments row/column normalized scores with 2D neighborhood context and adaptive coefficients located via proxy-based hyperparameter optimization.

If this is right

  • Higher retained accuracy in pruned LLMs at the same sparsity levels compared with RIA and other PTP baselines.
  • Hyperparameter search time reduced from roughly 11 hours to 20 minutes.
  • Discovered adaptive coefficients transfer directly to new models without additional search.
  • Orthogonal gains when combined with channel permutation, non-uniform sparsity, and re-pruning techniques.

Where Pith is reading between the lines

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

  • The 2D neighborhood term implies that weight matrices contain local spatial structure worth exploiting for importance estimation.
  • Proxy optimization may lower costs when tuning other pruning or compression hyperparameters.
  • Transferability of coefficients suggests they capture architecture-agnostic properties of transformer weight distributions.
  • The method could reduce the barrier to deploying high-sparsity LLMs on resource-constrained hardware.

Load-bearing premise

That incorporating 2D neighborhood context and adaptive row-column coefficients will reliably produce more accurate pruning decisions that generalize without retraining or model-specific retuning beyond the searched coefficients.

What would settle it

A comparison on a held-out LLM architecture and sparsity level in which CRePE-pruned models exhibit equal or higher perplexity than RIA-pruned models at identical sparsity.

Figures

Figures reproduced from arXiv: 2606.01544 by Cheonjun Park.

Figure 1
Figure 1. Figure 1: Importance score computation of (a) Magnitude Pruning, (b) Wanda, (c) RIA, and (d) CRePE. CRePE [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Ablation on directional contributions. PPL [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of PHO (Proxy-based Hyperparameter Optimization). [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

Deploying Large Language Models (LLMs) in practice incurs substantial memory and computational costs. Post-training pruning (PTP) is an effective approach to reducing these costs by removing weights without additional training. Among existing methods, RIA introduces relative importance scores normalized by row and column sums, achieving state-of-the-art accuracy. However, RIA considers only 1D cross-shaped (row/column) directional information and assigns equal weight to row and column contributions. In this paper, we propose \textbf{CRePE}, which incorporates 2D local neighborhood context and adaptive coefficients into Relative Importance scoring. CRePE consistently outperforms existing PTP methods across diverse models and sparsity settings. However, identifying optimal adaptive coefficients via perplexity (PPL)-based hill climbing requires numerous PPL evaluations and approximately 11 hours of search time. To address this, we propose \textbf{PHO} (Proxy-based Hyperparameter Optimization), which eliminates the need for repeated PPL measurements and reduces the search time to approximately 20 minutes. Furthermore, the optimal hyperparameter configuration found by PHO on one model transfers well to other models, demonstrating strong generalization. Finally, we verify that CRePE can be orthogonally combined with existing techniques including Channel Permutation, non-uniform sparsity allocation, and re-pruning methods.

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 CRePE, an extension to relative importance scoring (RIA) for post-training pruning of LLMs that adds 2D local neighborhood context and adaptive coefficients for row/column contributions. It introduces PHO, a proxy-based hyperparameter optimization method that reduces the cost of searching for these coefficients from ~11 hours (PPL-based hill climbing) to ~20 minutes. The manuscript claims that CRePE consistently outperforms prior PTP methods across models and sparsity levels, that the coefficients found by PHO transfer across models, and that CRePE combines orthogonally with channel permutation, non-uniform sparsity, and re-pruning.

Significance. If the empirical claims hold, the work offers a practical advance in PTP by improving importance scoring accuracy while addressing the computational cost of adaptive coefficient search via PHO; the reported transferability of hyperparameters across models would reduce per-model tuning overhead. No machine-checked proofs or parameter-free derivations are present, but the efficiency gain and orthogonal-combinations claim are scoped appropriately.

major comments (1)
  1. [PHO and Experiments] The central performance claim depends on coefficients obtained by search (PHO) on a proxy for perplexity; the manuscript must demonstrate that the proxy correlates sufficiently with true PPL on held-out data or across multiple models to ensure the reported gains are not an artifact of the search procedure itself (PHO section and experimental tables).
minor comments (2)
  1. [Abstract] Abstract: quantitative results, baseline names, and error bars are referenced only qualitatively; the main text should ensure all tables include these for the 'consistent outperformance' claim.
  2. [Method] Notation for the 2D neighborhood and adaptive coefficients should be formalized with explicit equations early in the method section to aid reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and the minor revision recommendation. We address the major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [PHO and Experiments] The central performance claim depends on coefficients obtained by search (PHO) on a proxy for perplexity; the manuscript must demonstrate that the proxy correlates sufficiently with true PPL on held-out data or across multiple models to ensure the reported gains are not an artifact of the search procedure itself (PHO section and experimental tables).

    Authors: We agree that explicit validation of the proxy's correlation with true PPL is necessary to substantiate that the reported gains are not artifacts of the search. The current manuscript demonstrates efficiency gains and cross-model transfer of the resulting coefficients but does not include a dedicated correlation analysis. In the revised version we will add quantitative results (e.g., Pearson/Spearman correlations and scatter plots) in the PHO section, computed on held-out data for the models used in the experiments. This addition will directly address the concern while preserving the paper's scope. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper describes an empirical post-training pruning method (CRePE) that augments an existing RIA baseline with 2D neighborhood context and searched adaptive coefficients, plus a proxy (PHO) to accelerate hyperparameter search. Performance claims rest on experimental comparisons after standard hyperparameter tuning on a proxy metric, with reported transfer across models; this does not constitute a derivation chain that reduces to self-definition, fitted inputs renamed as predictions, or load-bearing self-citations. No equations or uniqueness theorems are invoked that collapse by construction to the inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that 2D neighborhood information improves importance scoring and on free parameters (adaptive coefficients) whose values are determined by search on model outputs.

free parameters (1)
  • adaptive coefficients
    Values chosen via PPL hill climbing or PHO proxy to balance row and column contributions for each model and sparsity level.
axioms (1)
  • domain assumption 2D local neighborhood context improves relative importance scoring over 1D row/column normalization
    Invoked as the motivation for moving beyond RIA's cross-shaped directional information.

pith-pipeline@v0.9.1-grok · 5757 in / 1217 out tokens · 36761 ms · 2026-06-28T15:41:21.471058+00:00 · methodology

discussion (0)

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