arXiv Machine Learning
Oct 3

Trust the Direction, Search the Step: Zero-and-First-Order Methods for LLM Fine-Tuning

The paper introduces ZFO, a lightweight framework that separates direction selection from step-size determination in large‑scale neural network optimization. ZFO uses a trusted first‑order optimizer to pick a search direction and then performs only two additional objective evaluations to build a local curvature‑aware model, selecting an adaptive step within a bounded interval. The authors provide theoretical guarantees for reliable curvature estimation, near‑optimal step selection, and convergence to a stationary point, and demonstrate that ZFO improves optimization and final performance over fixed‑step first‑order baselines on language‑model fine‑tuning tasks.

By Cristian McGee, El Houcine Bergou, Aritra Dutta
arXiv Machine Learning
Jul 2

Zeroth-Order Optimization at the Edge of Stability

arXiv:2604. 14669v2 Announce Type: replace Abstract: Zeroth-order (ZO) methods are widely used when gradients are unavailable or prohibitively expensive, including black-box learning and memory-efficient fine-tuning of large models, yet their optimization dynamics in deep learning remain underexplored.

By Minhak Song, Liang Zhang, Bingcong Li, Niao He, Michael Muehlebach, Sewoong Oh
arXiv Machine Learning
Oct 5

Near-Optimal Convex Optimization with Lazy Second-Order Oracles

arXiv:2610. 03222v1 Announce Type: cross Abstract: This paper studies the complexity of convex optimization using lazy second-order oracles (Doikov, Chayti, and Jaggi, ICML 2023), where an algorithm queries gradients every iteration and Hessians once per $m$ iterations.

By Xinliang Zhang, Lesi Chen, Chengchang Liu, Jingzhao Zhang