arXiv (All)
open access
ID Balancing: Stable Training of Extremely Sparse MoE via PID-Based Load Control
Scaling Large Language Models (LLMs) via Mixture-of-Experts (MoE) enables massive parameter growth with nearly constant per-token computation. However, further scaling the parameter count requires increasingly sparse routing, where expert load imbalance becomes more severe. This imbalance reduces pa
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
- Accuracy of Training Data and Model Outputs in Generative AI: CREATe Response to the Information Commissioner’s Office (ICO) Consultation#818474
- Routing Should Pay for Itself: Sparse Supervision for Economical LLM Routing#1141724
- Conditioned Initialization for Attention#975150
- AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control#1132177
- Continual Learning Mechanisms Compose for Long-Horizon Memorization#975059
Record · ID 1144104
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