arXiv (All)
open access
Exact Quantile Balancing and Load-Error Injection for Mixture-of-Experts
Mixture-of-Experts (MoE) training requires global load balance to prevent expert under-utilization and local balance for efficient expert-parallel execution. Existing distributed Quantile Balancing (QB) uses shard-dependent or approximate global quantiles, while token-independent expert biases canno
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
- Moebius: Serving Mixture-of-Expert Models with Seamless Runtime Parallelism Switch#310786
- Surviving Partial Rank Failures in Wide Expert-Parallel MoE Inference#175195
- UniEP: Unified Expert-Parallel MoE MegaKernel for LLM Training#124027
- Diverse Geometries, Frozen Weights: Robust Heterogeneous Treatment-Effect Estimation via Causal Expert Ensembles#1078045
- Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods#179161
Record · ID 1064597
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