arXiv (All)
open access
UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models
Sparse mixture-of-experts (MoE) layers expand recommendation capacity through conditional computation, yet a trained checkpoint still stores and routes over its full expert bank. We study a deployment problem: convert that checkpoint to a smaller standard MoE under an explicit expert budget, without
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Training-Free Halving of Activated Experts in Fine-Grained Mixture-of-Experts Models#927851
- Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models#1144863
- Flattening Every Memory Peak in Long-Context Mixture-of-Experts Training#1083758
- Flattening Every Memory Peak in Long-Context Mixture-of-Experts Training#919362
- The Limits of Speculation: Bounding Speculative Decoding in Mixture-of-Experts#1036839
Record · ID 985201
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive