arXiv (OAI Expanded)
open access
LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that treats every LoRA step as a tangent vector of the fixed-ran
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
- LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates#924870
- Diffract: Spectral View of LLM Domain Adaptation#444645
- Behavior Quotient Learning for Low-Rank Adaptation of LLM Agents#1070348
- Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation#1142672
- SMEPilot: Characterizing and Optimizing LLM Inference with Scalable Matrix Extensions#280164
Record · ID 920636
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