OMP-MoE: Efficient Expert Pruning for Mixture-of-Experts LLMs via Orthogonal Matching Pursuit
cs.LG
Submitted: 2026-09-09
Updated: 2026-09-09
Terminology
Sources
- GPT-4 Technical Report
- gpt-oss-120b & gpt-oss-20b Model Card
- DiEP: Adaptive Mixture-of-Experts Compression through Differentiable Expert Pruning
- Evaluating Large Language Models Trained on Code
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- Training Verifiers to Solve Math Word Problems
- DeepSeek-V3 Technical Report
- The Llama 3 Herd of Models
- Hierarchical Sparse Attention Done Right: Toward Infinite Context Modeling
- Mixtral of Experts
- HEAPr: Hessian-based Efficient Atomic Expert Pruning in Output Space
- Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
- DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
- Pointer Sentinel Mixture Models
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
- Unveiling Super Experts in Mixture-of-Experts Large Language Models
- Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them
- Qwen3 Technical Report
- MoNE: Replacing Redundant Experts with Lightweight Novices for Structured Pruning of MoE
- Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning
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