arXiv (All)
open access
PowerStep: Memory-Efficient Adaptive Optimization via $\ell_p$-Norm Steepest Descent
artificial-intelligencecomputation-and-languagemachine-learningnumerical-analysisoptimization-and-control
Adaptive optimizers such as Adam are standard for training Transformers, but storing gradient first and second moments incurs substantial memory overhead. We introduce PowerStep, a memory-efficient optimizer that achieves coordinate-wise adaptivity without storing second-moment statistics. Motivated
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
- Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training#194280
- AdaProb: Efficient Machine Unlearning via Adaptive Probability#128319
- HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training#1118282
- A Novel Multi-fidelity Surrogate for Turbomachinery Design Optimization#998666
- Adaptive multi-fidelity optimization with fast learning rates#31260
Record · ID 1131527
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