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Curvature-Conditioned Multiscale Momentum with Sphere Constraints for LLM Pretraining

Paper recorded by Signals 4 on 2026-08-28 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-28 on arXiv · recorded by Signals 4 on 2026-08-31

Category: cs.LG · 机器学习 · first seen 2026-08-31

Abstract

Pretraining accounts for a large fraction of the total computational cost in LLM training. However, noise-dominant gradients and the highly ill-conditioned loss landscape bring severe challenges. Although modern adaptive optimizers such as AdamW and Muon have achieved great success in large-scale pretraining, their reliance on gradient normalization offers limited mitigation of the ill-conditioned

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#210 most recent of 215 cs.LG papers we have recorded · ↑ newer: Sliding-window beats linear attention · ↓ older: Euclidean Fourier Neural Operators
Cite this page: Curvature-Conditioned Multiscale Momentum with Sphere Constraints for LLM Pretraining: the #210 most recent of 215 cs.LG papers we have recorded (as of 2026-08-28). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/curvature-conditioned-multiscale-momentum-with-sphere-constraints-for-llm-pretra.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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