Cover Image

ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yadav, Prateek, Choshen, Leshem, Raffel, Colin, Bansal, Mohit
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
by: Yadav, Prateek, et al.
Published: (2024)

RSQ: Learning from Important Tokens Leads to Better Quantized LLMs
by: Sung, Yi-Lin, et al.
Published: (2025)

Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
by: Li, Pingzhi, et al.
Published: (2023)

Merging by Matching Models in Task Parameter Subspaces
by: Tam, Derek, et al.
Published: (2023)

Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion
by: Zaman, Kerem, et al.
Published: (2023)

A Hitchhiker's Guide to Scaling Law Estimation
by: Choshen, Leshem, et al.
Published: (2024)

Task-Circuit Quantization: Leveraging Knowledge Localization and Interpretability for Compression
by: Xiao, Hanqi, et al.
Published: (2025)

Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings
by: Jiang, Yichen, et al.
Published: (2024)

AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
by: Zhou, Han, et al.
Published: (2023)

What Matters for Model Merging at Scale?
by: Yadav, Prateek, et al.
Published: (2024)

Position: The Most Expensive Part of an LLM should be its Training Data
by: Kandpal, Nikhil, et al.
Published: (2025)

X-PEFT: eXtremely Parameter-Efficient Fine-Tuning for Extreme Multi-Profile Scenarios
by: Kwak, Namju, et al.
Published: (2024)

Robustness as an Emergent Property of Task Performance
by: Ashury-Tahan, Shir, et al.
Published: (2026)

tinyBenchmarks: evaluating LLMs with fewer examples
by: Polo, Felipe Maia, et al.
Published: (2024)

MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba
by: Yoshimura, Masakazu, et al.
Published: (2024)

Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty
by: Damani, Mehul, et al.
Published: (2025)

Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
by: Brüel-Gabrielsson, Rickard, et al.
Published: (2024)

Think Right: Learning to Mitigate Under-Over Thinking via Adaptive, Attentive Compression
by: Singh, Joykirat, et al.
Published: (2025)

Efficient multi-prompt evaluation of LLMs
by: Polo, Felipe Maia, et al.
Published: (2024)

TextArena
by: Guertler, Leon, et al.
Published: (2025)

Finer Parameter Steps for Low-Rank PEFT: A Controlled Study with CP Tensor Adapters
by: Wang, Xinjue, et al.
Published: (2026)

Genie: Achieving Human Parity in Content-Grounded Datasets Generation
by: Yehudai, Asaf, et al.
Published: (2024)

Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models
by: Peng, Zhiyuan, et al.
Published: (2024)

Resolving Interference (RI): Disentangling Models for Improved Model Merging
by: Ramesh, Pratik, et al.
Published: (2026)

ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs
by: Chen, Justin Chih-Yao, et al.
Published: (2023)

Multi-Attribute Steering of Language Models via Targeted Intervention
by: Nguyen, Duy, et al.
Published: (2025)

EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents
by: Zala, Abhay, et al.
Published: (2024)

UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning
by: Patil, Vaidehi, et al.
Published: (2025)

Prune-Quantize-Distill: An Ordered Pipeline for Efficient Neural Network Compression
by: Zhou, Longsheng, et al.
Published: (2026)

COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling
by: Flynn, Noah
Published: (2026)

The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
by: Hase, Peter, et al.
Published: (2024)

On the Compressibility of Quantized Large Language Models
by: Mao, Yu, et al.
Published: (2024)

AMAQ: Adaptive Mixed-bit Activation Quantization for Collaborative Parameter Efficient Fine-tuning
by: Song, Yurun, et al.
Published: (2025)

PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs
by: Yu, Tongzhou, et al.
Published: (2025)

Efficient Benchmarking of Language Models
by: Perlitz, Yotam, et al.
Published: (2023)

Enhancing Delta Compression in LLMs via SVD-based Quantization Error Minimization
by: Xiong, Boya, et al.
Published: (2025)

Soft Self-Consistency Improves Language Model Agents
by: Wang, Han, et al.
Published: (2024)

DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
by: Khan, Zaid, et al.
Published: (2024)

CHESS: Optimizing LLM Inference via Channel-Wise Thresholding and Selective Sparsification
by: He, Junhui, et al.
Published: (2024)

Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills
by: Chen, Justin Chih-Yao, et al.
Published: (2025)