Shared Low-rank Basis Factorization for Data-free Mixture-of-Experts Compression
cs.LG, cs.AI
Submitted: 2026-10-07
Updated: 2026-10-07
Comments: Accepted to Findings of EMNLP 2026
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Program Synthesis with Large Language Models
- Task-Specific Expert Pruning for Sparse Mixture-of-Experts
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- Training Verifiers to Solve Math Word Problems
- Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
- REAM: Merging Improves Pruning of Experts in LLMs
- Mixtral of Experts
- DeepSeek-V3 Technical Report
- Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs
- Muon is Scalable for LLM Training
- MoLAE: Mixture of Latent Experts for Parameter-Efficient Language Models
- Qwen3 Technical Report
- Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts
- MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
- Instruction-Following Evaluation for Large Language Models
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks