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Learning Semi-Structured Sparsity for LLMs via Shared and Context-Aware Hypernetwork

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
LLM pruningsemi-structured sparsityhypernetworkcontinual learning
Abstract:

Large Language Models (LLMs) achieve state-of-the-art performance but are costly to deploy in resource-constrained environments. Pruning with n:mn:m semi-structured sparsity reduces computation and enables hardware acceleration, yet existing methods face a trade-off: one-shot approaches are efficient but heuristic, while optimization-based methods are accurate but expensive.
We introduce \textbf{HyperPrune}, a resource-efficient framework that directly optimizes n:mn:m sparsity. A lightweight hypernetwork, shared across layers and conditioned on learnable embeddings, generates structured masks in a one-shot, layer-wise manner. \textit{Continual pruning} preserves cross-layer knowledge, and \textit{feature outlier regularization} retains critical activations, unifying the strengths of heuristic and optimization-based methods.
Experiments on LLaMA-7B to 70B show state-of-the-art accuracy–sparsity trade-offs on a single A100 GPU, achieving higher efficiency, accuracy, and scalability than prior approaches. HyperPrune offers a practical, scalable, and hardware-friendly solution for structured LLM pruning.

Disclaimer
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

HyperPrune introduces a hypernetwork-based framework for learning n:m semi-structured sparsity in large language models, combining one-shot efficiency with optimization-based accuracy. The paper sits within the 'End-to-End Learnable Mask Optimization' leaf of the taxonomy, which contains five papers total including this work. This leaf represents a moderately active research direction focused on differentiable mask selection during training, distinguishing itself from heuristic post-training methods and architectural-specific approaches in neighboring taxonomy branches.

The taxonomy reveals that HyperPrune's immediate neighbors include methods like MaskLLM, ProxSparse, and MaskPro, all exploring learnable mask optimization but with different parameterization strategies. Adjacent leaves address 'Structured Sparsity with Architectural Dependencies' (incorporating GLU-specific considerations) and 'Low-Rank and Sparse Hybrid Compression' (combining low-rank decomposition with sparsity). The broader 'Semi-Structured (N:M) Sparsity Learning Methods' branch contrasts with the larger 'Post-Training Pruning for LLMs' branch, which encompasses one-shot methods like SparseGPT and Wanda that operate without retraining—a key distinction from HyperPrune's optimization-based approach.

Among twelve candidates examined across three contributions, no clear refutations emerged. The core HyperPrune framework examined two candidates with zero refutable overlaps, suggesting the hypernetwork-based mask generation approach may offer a distinct parameterization strategy. The information-theoretic justification examined ten candidates without refutation, though this does not confirm absolute novelty given the limited search scope. The regularization techniques contribution examined zero candidates, leaving its novelty assessment incomplete within this analysis.

Based on the limited literature search of twelve candidates, HyperPrune appears to occupy a recognizable position within learnable mask optimization, proposing a hypernetwork-based parameterization that differs from sibling approaches. The analysis covers top-K semantic matches and does not constitute an exhaustive survey of all related work in semi-structured sparsity or hypernetwork-based compression methods.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
12
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Learning semi-structured sparsity for large language models. The field of sparsity for LLMs has evolved into a rich taxonomy with several major branches, each addressing distinct aspects of model compression and efficiency. Semi-Structured (N:M) Sparsity Learning Methods focus on hardware-friendly patterns where N out of every M consecutive weights are non-zero, enabling practical acceleration on modern GPUs. Post-Training Pruning for LLMs encompasses techniques like SparseGPT[29] and Wanda that remove weights after pre-training without extensive retraining, while Activation-Based Dynamic Sparsity methods such as Deja Vu[16] exploit runtime sparsity in activations. Parameter-Efficient Fine-Tuning with Sparsity combines approaches like LongLoRA[6] and sparse adapters to maintain efficiency during task adaptation, whereas Pruning-Aware Pre-Training and Training integrates sparsity constraints directly into the training process. Specialized Sparsity Techniques and Extensions cover domain-specific methods, including attention sparsity patterns and architectural innovations. Within the semi-structured sparsity branch, a particularly active line of work centers on end-to-end learnable mask optimization, where methods like MaskLLM[1], ProxSparse[7], and MaskPro[8] learn which weights to prune jointly with model parameters. Shared Hypernetwork Sparsity[0] sits squarely in this cluster, emphasizing efficient mask generation through shared hypernetworks that reduce the overhead of maintaining separate pruning decisions across layers. This contrasts with approaches like Lost[3] that may rely on gradient-based importance scores or ProxSparse[7] that employs proximal optimization for mask learning. The central tension across these works involves balancing the expressiveness of learned masks against computational overhead during training and inference, with Shared Hypernetwork Sparsity[0] proposing a parameter-sharing strategy that aims to achieve competitive sparsity patterns while minimizing the additional cost of mask parameterization.

Claimed Contributions

HyperPrune framework for n:m semi-structured sparsity

The authors propose HyperPrune, a framework that uses a shared lightweight hypernetwork conditioned on context-aware embeddings to generate n:m structured masks for LLM pruning in a layer-wise manner, enabling efficient optimization of semi-structured sparsity patterns.

2 retrieved papers
Information-theoretic justification for n:m pruning

The authors establish a theoretical connection showing that maximizing mutual information between dense and pruned models under n:m sparsity constraints is equivalent to minimizing reconstruction loss through differentiable relaxation, providing a principled foundation for structured mask optimization.

10 retrieved papers
Regularization techniques for feature and knowledge preservation

The authors introduce two novel regularization methods: feature outlier regularization that preserves weights associated with high-magnitude activations, and continual pruning regularization that maintains cross-layer knowledge during sequential layer-wise pruning to prevent catastrophic forgetting.

0 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

HyperPrune framework for n:m semi-structured sparsity

The authors propose HyperPrune, a framework that uses a shared lightweight hypernetwork conditioned on context-aware embeddings to generate n:m structured masks for LLM pruning in a layer-wise manner, enabling efficient optimization of semi-structured sparsity patterns.

Contribution

Information-theoretic justification for n:m pruning

The authors establish a theoretical connection showing that maximizing mutual information between dense and pruned models under n:m sparsity constraints is equivalent to minimizing reconstruction loss through differentiable relaxation, providing a principled foundation for structured mask optimization.

Contribution

Regularization techniques for feature and knowledge preservation

The authors introduce two novel regularization methods: feature outlier regularization that preserves weights associated with high-magnitude activations, and continual pruning regularization that maintains cross-layer knowledge during sequential layer-wise pruning to prevent catastrophic forgetting.

Table of Contents

Learning Semi-Structured Sparsity for LLMs via Shared and Context-Aware Hypernetwork | Novelty Validation