arXiv Machine Learning
Jun 11

Mirror Descent Beyond Euclidean Stability: An Exponential Separation in Initialization Sensitivity

arXiv:2606. 11431v1 Announce Type: new Abstract: Mirror Descent (MD) extends Gradient Descent (GD) beyond Euclidean geometry and has recently reappeared as a lens for KL-regularized policy optimization in reinforcement learning and LLM post-training.

By Shira Vansover-Hager, Matan Schliserman, Ofir Schlisselberg, Tomer Koren
arXiv Machine Learning
Aug 26

Finite-Sample Metric Non-Collapse for Geometrically Supervised Latent World Models in Control

The paper presents a finite‑sample learning‑to‑control framework for geometrically supervised latent models of nonlinear deterministic systems. It introduces an encoder‑only local–global metric hinge that ensures directional resolution and state discrimination, and proves that any approximate empirical minimizer is pointwise co‑Lipschitz and uniformly approximately semiconjugate to the true dynamics under regularity assumptions. The results provide explicit bounds on approximation, sampling, and optimization errors, and demonstrate through controlled experiments that restoring metric resolution improves control performance.

By Alain Bensoussan, Minh-Nhat Phung, Minh-Binh Tran