arXiv (All)
open access
AIM-ZO: Activation-Informed Subspace Maintenance for Zeroth-Order LLM Fine-Tuning
Zeroth-order (ZO) optimization offers a memory-efficient alternative for LLM fine-tuning by estimating updates only from forward evaluations of perturbed parameters, without backpropagation or activation storage. However, in billion-parameter LLMs, isotropic perturbations often waste many forward ev
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
- SubZero+: Efficient Zeroth-Order LLM Fine-Tuning via Large Learning Rates#616813
- SubZero+: Efficient Zeroth-Order LLM Fine-Tuning via Large Learning Rates#616313
- Trust the Direction, Search the Step: Zero-and-First-Order Methods for LLM Fine-Tuning#1161018
- Trust the Direction, Search the Step: Zero-and-First-Order Methods for LLM Fine-Tuning#1157538
- ZeroLock: Concurrent Memory-Efficient LLM Training via Modular Update Decoupling#441772
Record · ID 1129851
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