arXiv Machine Learning
Jul 16
DarwinLM: Evolutionary Structured Pruning of Large Language Models
arXiv:2502. 07780v4 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved significant success across various NLP tasks.
Small LLMs: Pruning vs. Training from Scratch
arXiv:2606. 14150v1 Announce Type: new Abstract: Pruning promises a shortcut to strong small language models.
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arXiv Machine Learning
Jul 14
Prune, Update and Trim: Robust Structured Pruning for Large Language Models
arXiv:2605. 18331v2 Announce Type: replace Abstract: Large Language Models (LLMs) have experienced significant growth and development in recent years.
arXiv Machine Learning
Aug 11
High-Layer Attention Pruning with Rescaling
arXiv:2507. 01900v3 Announce Type: replace-cross Abstract: Pruning is a highly effective approach for compressing large language models (LLMs), significantly reducing inference latency.
arXiv Machine Learning
Jun 29
FlexMoE: One-for-All Nested Intra-Expert Pruning for MoE Language Models
arXiv:2606. 27866v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models scale model ability with sparsely activated experts, making this architecture a standard recipe for modern large models.
arXiv Machine Learning
5d ago
Unifying Depth and Width Pruning for LLMs via Binary Knapsack Optimization
arXiv:2608. 12953v1 Announce Type: cross Abstract: Structured pruning is a promising approach for compressing large language models (LLMs), yet existing methods rely heavily on greedy heuristics that produce myopic decisions, and often fail to precisely meet target compression budgets.
arXiv Machine Learning
Jun 9
Beyond FLOPs: Benchmarking Real Inference Acceleration of LLM Pruning under a GEMM-Centric Taxonomy
arXiv:2606. 09080v1 Announce Type: new Abstract: Pruning has emerged as a dominant paradigm for accelerating large language model (LLM) inference, spanning a broad spectrum of methods that remove computation across tokens, layers, heads, dimensions, and attention patterns.
arXiv AI
Jul 1
One Shot vs. Iterative: Rethinking Pruning Strategies for Model Compression
arXiv:2508. 13836v2 Announce Type: replace-cross Abstract: Pruning is a core technique for compressing neural networks to improve computational efficiency.
arXiv AI
Aug 11
Length-MAX Tokenizer for Language Models
arXiv:2511. 20849v2 Announce Type: replace-cross Abstract: We introduce a new tokenizer for language models that minimizes the average tokens per character, thereby reducing the number of tokens needed to represent text during training and to generate text during inference.
arXiv AI
Aug 5
On the Limits of Layer Pruning for Generative Reasoning in Large Language Models
arXiv:2602. 01997v3 Announce Type: replace-cross Abstract: Recent work has shown that layer pruning can effectively compress large language models (LLMs) while retaining strong performance on classification benchmarks, often with little or no finetuning.
arXiv Machine Learning
Jun 11
Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning
arXiv:2606. 12278v1 Announce Type: cross Abstract: Neural network pruning reduces model size by removing less important parameters while aiming to preserve predictive performance.
arXiv AI
Jul 22
Beyond Single-Dimensional Compression: The Compound Sparsity Frontier of Large Language Models
arXiv:2607. 18280v1 Announce Type: cross Abstract: Large language models (LLMs) are often compressed through static parameter pruning or dynamic token-level computation, yet aggressive sparsification can trigger rapid performance degradation beyond an essential sparsity boundary.
arXiv Machine Learning
Jul 31
WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
arXiv:2607. 28418v1 Announce Type: cross Abstract: Pruning is a promising approach for improving the efficiency of LLMs.