arXiv (All)
open access
EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training
Training large language models (LLMs) at scale incurs substantial communication overhead, while static gradient compression cannot adapt to gradient evolution and may degrade model quality. We propose EDGC, an entropy-driven dynamic gradient compression framework that adapts compression ranks to gra
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
- Uncheatable Eval: Dynamic Compression-Based Evaluation of Language Models#1064054
- Amortizing intractable inference in large language models#798540
- Amortizing intractable inference in large language models#797127
- GeLaCo: An Evolutionary Approach to Layer Compression#1013429
- Detecting Pretraining Data in Large Language Models from a Free-Energy Perspective#1036277
Record · ID 1143639
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