arXiv Machine Learning
AURA: Angular Update Rate Adaptation for training complex-valued neural networks
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Hyperball May Not Be a Free Lunch
arXiv:2607. 22444v1 Announce Type: new Abstract: For scale-invariant deep networks, Hyperball-style optimizers have shown strong performance in large-scale training by fixing the norms of matrix-valued parameters and normalizing updates.
arXiv Machine Learning
Jun 25
Improving Neural Network Training by Decoupling the Magnitude and Direction of Weight Vectors
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arXiv AI
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Learning in Curved Weight Space:Exponential-Linear Weight Reparameterization for Improved Optimization
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arXiv Machine Learning
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Towards Stability of Parameter-Free Optimization
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arXiv Machine Learning
Aug 4
AOS: Adaptive Optimizer Switching via Training-State Signals for Faster Convergence and Better Generalization
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arXiv Machine Learning
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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.