OpenNovelty.org

GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Large Language modelsmodel pruning
Abstract:

Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. However, such impressive capability typically comes with a substantial model size, which presents significant challenges in deployment and inference. While structured pruning of model parameters offers a promising way to reduce computational costs at deployment time, current methods primarily focus on single model pruning. In this work, we develop a novel strategy to compress models by strategically combining or merging layers from finetuned model variants, which preserves the original model's abilities by aggregating capabilities accentuated in different finetunes. We pose the optimal tailoring of these LLMs as a zero-order optimization problem, adopting a search space that supports three different operations: (1) Layer removal, (2) Layer selection from different candidate models, and (3) Layer merging. Our experiments demonstrate that this approach leads to competitive model pruning, for example, for the Llama2-13B model families, our compressed models maintain approximately 97.3% of the original performance while removing ～25% of parameters, significantly outperforming previous state-of-the-art methods.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a structured pruning method that combines layer removal, layer selection from multiple finetuned variants, and layer merging via zero-order optimization. It resides in the 'Layer Collapse and Merging' leaf under 'Depth Pruning and Layer Removal', which contains only two papers including this one. This leaf is relatively sparse compared to more crowded branches like 'Weight Magnitude-based Pruning' or 'Layer-wise Reconstruction-based Pruning', suggesting the specific approach of merging layers from model families is less explored than single-model pruning strategies.

The taxonomy reveals neighboring directions such as 'Direct Layer Removal' and 'Layer Concatenation and Aggregation', which also reduce depth but differ in mechanism. The sibling paper in the same leaf likely shares the layer-merging philosophy but may use different optimization frameworks or merging criteria. Nearby branches like 'Activation-based Importance' and 'Adaptive Layer-wise Sparsity Allocation' address complementary questions of redundancy measurement and budget distribution, while 'Integration with Parameter-Efficient Fine-tuning' explores orthogonal compression strategies. The paper's focus on multi-model aggregation distinguishes it from these single-model or parameter-tuning approaches.

Among thirty candidates examined, the zero-order optimization framework (Contribution A) and search space design (Contribution C) show no clear refutations across ten candidates each, suggesting these aspects may be relatively novel within the limited search scope. However, the training-free pruning claim (Contribution B) encounters six refutable candidates among ten examined, indicating substantial prior work on retraining-free methods exists in branches like 'Training-free and Retraining-free Pruning'. The statistics suggest the multi-model merging angle is less contested than the training-free aspect, though the search examined only a fraction of the field's fifty papers.

Based on this limited analysis of thirty semantically similar candidates, the work appears to occupy a moderately novel position by combining model-family merging with zero-order search, though the training-free claim overlaps with existing methods. The taxonomy structure indicates the specific leaf is sparse, but the broader depth-pruning branch is well-populated, and the analysis does not cover all related directions exhaustively. A more comprehensive search might reveal additional overlaps or confirm the novelty of the multi-model aggregation strategy.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
6
Refutable Paper

Research Landscape Overview

Core task: structured pruning of large language models through layer-wise optimization. The field has evolved into a rich taxonomy reflecting diverse strategies for compressing LLMs while preserving performance. Major branches include layer-wise reconstruction-based pruning, which rebuilds activations after removing structures; gradient-based and activation-based importance estimation, which score components for removal; weight magnitude-based pruning, a classical approach adapted to modern scales; and adaptive layer-wise sparsity allocation, which distributes pruning budgets non-uniformly. Additional directions encompass depth pruning and layer removal, which target entire layers or merge redundant ones; structured unit selection, focusing on attention heads or feed-forward neurons; semi-structured sparsity patterns like N:M pruning; integration with parameter-efficient fine-tuning methods such as LoRA; and training-free or retraining-free techniques that avoid costly post-pruning updates. Works like LLM-Pruner[5] and SlimGPT[3] exemplify reconstruction-based and gradient-driven approaches, while Shortgpt[12] and BESA[20] illustrate depth reduction strategies. Within this landscape, depth pruning and layer removal have attracted considerable attention, with methods exploring whether to drop entire layers or collapse adjacent ones. GPTailor[0] sits squarely in the layer collapse and merging cluster, emphasizing the fusion of similar layers to reduce depth without discarding learned representations entirely. This contrasts with approaches like LaCo[19], which also merges layers but may differ in the criteria or optimization framework used to identify mergeable pairs. Meanwhile, branches such as activation-based redundancy analysis and adaptive sparsity allocation tackle orthogonal questions: how to measure which structures are truly redundant and how to allocate pruning budgets across heterogeneous layers. The interplay between these directions—whether to remove layers outright, merge them, or prune within them—remains an active area of exploration, with trade-offs involving computational cost, retained accuracy, and the need for retraining.

Claimed Contributions

Novel structured pruning method via zero-order optimization over model families

The authors introduce a pruning approach that treats compression as an optimization problem over multiple fine-tuned variants of a base model rather than pruning a single model. The method supports three operations: layer removal, layer selection from different candidate models, and layer merging, using zero-order search to find optimal configurations.

10 retrieved papers
Cost-effective pruning without post-training requirement

The authors demonstrate that their method achieves effective compression without requiring expensive post-training procedures to recover performance, unlike conventional pruning methods that typically need additional fine-tuning after pruning.

10 retrieved papers
Can Refute
Search space design supporting layer cutting and stitching operations

The authors design a search space formulation that enables combining layers from multiple fine-tuned model variants through removal, selection, and merging operations. This allows the pruned model to aggregate capabilities accentuated in different task-specific fine-tunes.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Novel structured pruning method via zero-order optimization over model families

The authors introduce a pruning approach that treats compression as an optimization problem over multiple fine-tuned variants of a base model rather than pruning a single model. The method supports three operations: layer removal, layer selection from different candidate models, and layer merging, using zero-order search to find optimal configurations.

Contribution

Cost-effective pruning without post-training requirement

The authors demonstrate that their method achieves effective compression without requiring expensive post-training procedures to recover performance, unlike conventional pruning methods that typically need additional fine-tuning after pruning.

Contribution

Search space design supporting layer cutting and stitching operations

The authors design a search space formulation that enables combining layers from multiple fine-tuned model variants through removal, selection, and merging operations. This allows the pruned model to aggregate capabilities accentuated in different task-specific fine-tunes.

Table of Contents