AI RESEARCH
Revisiting Zeroth-Order Hessian Approximation: A Single-Step Policy Optimization Lens
arXiv CS.LG
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ArXi:2605.30960v1 Announce Type: new Accurate Zeroth-Order (ZO) Hessian estimation is a cornerstone of derivative-free methods, essential for tasks such as bilevel optimization, Bayesian inference, and uncertainty quantification. However, obtaining a complete suite of low-variance estimators for the Hessian and its inverse in high-dimensional settings remains a significant challenge. To address this, we propose a unified framework that reinterprets ZO Hessian approximation through the lens of single-step Policy Optimization (