arXiv (All)
open access
Beyond Dense Adam States: Adaptive Log-Space Quantization for Memory-Efficient Optimizers
Optimizer-state quantization is commonly designed for Adam's dense, parameter-aligned first- and second-moment arrays. This abstraction breaks for memory-efficient optimizers, whose states may be factored, confidence-modulated, or maintained in a projected space, so similar reconstruction error can
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
- Learning in Curved Weight Space:Exponential-Linear Weight Reparameterization for Improved Optimization#926118
- diffGrad: An Optimization Method for Convolutional Neural Networks#19746
- Function Over Form: Distributional Orthogonalization in Mixture-of-Experts with Replica Expert Mechanism#1116729
- Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning#361469
- Matrix AdaGrad: Row-wise and Column-wise Adaptive Subgradient Methods#1036204
Record · ID 820627
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