GeLaCo: An Evolutionary Approach to Layer Compression

arXiv:2507.10059 · cs.CL · Submitted 2025-07-14 · Read on arXiv

cs.CL

Submitted: 2025-07-14

Updated: 2026-09-16

Code: https://github.com/microsoft/TransformerCompression

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Pruning — 31× in this paper · explained in 4 episodes
• Large Language Models — 19× in this paper
• Quantization — 15× in this paper · explained in 12 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Distillation — 10× in this paper · explained in 2 episodes
• Perplexity — 10× in this paper · explained in 3 episodes
• Knowledge Distillation — 8× in this paper · explained in 11 episodes
• Multi-objective Optimization — 6× in this paper · explained in 4 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Basque Research and Technology Alliance — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Elias — 2× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 2× in this paper
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Evolutionary Algorithms — 1× in this paper
• Feed-Forward Network — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• SliceGPT: Compress Large Language Models by Deleting Rows and Columns
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
• The Llama 3 Herd of Models
• MiniLLM: On-Policy Distillation of Large Language Models
• Measuring Massive Multitask Language Understanding
• Towards Efficient Automatic Self-Pruning of Large Language Models
• QKD: Quantization-aware Knowledge Distillation
• PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation
• The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
• LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
• LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
• SpinQuant: LLM quantization with learned rotations
• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
• ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
• EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
• DarwinLM: Evolutionary Structured Pruning of Large Language Models
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• A Survey on Knowledge Distillation of Large Language Models
• BitNet: Scaling 1-bit Transformers for Large Language Models

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