arXiv Machine Learning
Jun 17
MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization
arXiv:2606. 17526v1 Announce Type: new Abstract: Efficient optimization is essential for training large language models.
AOS: Adaptive Optimizer Switching via Training-State Signals for Faster Convergence and Better Generalization
arXiv:2608. 01997v1 Announce Type: new Abstract: Single-optimizer training is a poor fit for the distinct phases of deep network optimization: adaptive methods handle noisy early gradients well but overshoot flat minima, while SGD with momentum generalizes better in the late phase but converges slowly early on.
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arXiv AI
Aug 6
MESH: Memory-Efficient Sinkhorn Optimization for Mixture-of-Experts Training
arXiv:2608. 04407v1 Announce Type: cross Abstract: Memory-efficient matrix optimizers such as Sinkhorn gradient descent remove most AdamW optimizer state for dense Transformer matrices, but direct application to Mixture-of-Experts (MoE) training is unreliable.
arXiv Machine Learning
Jun 10
AdaGC: Enhancing LLM Pretraining Stability via Adaptive Gradient Clipping
arXiv:2502. 11034v3 Announce Type: replace Abstract: Loss spikes remain a persistent obstacle in large-scale language model pretraining.
arXiv Machine Learning
Jul 31
Towards Stability of Parameter-Free Optimization
arXiv:2405. 04376v4 Announce Type: replace Abstract: Hyperparameter tuning, particularly the selection of an appropriate learning rate in adaptive gradient training methods, remains a challenge.
arXiv Machine Learning
Aug 4
GradientStabilizer:Fix the Norm, Not the Gradient
arXiv:2502. 17055v5 Announce Type: replace Abstract: Training instability in modern deep learning systems is frequently triggered by rare but extreme gradient-norm spikes, which can induce oversized parameter updates, corrupt optimizer state, and lead to slow recovery or divergence.
arXiv Machine Learning
Jul 13
LionVote: Per-Layer Learning Rate Adaptation for Lion
arXiv:2607. 09266v1 Announce Type: new Abstract: Per-layer diagnostics reveal that, at the prescribed learning rate, Lion's effective scale is 2.
arXiv AI
Jun 15
Gefen: Optimized Stochastic Optimizer
arXiv:2606. 13894v1 Announce Type: cross Abstract: AdamW is a default optimizer for modern deep learning, but its first and second moment states add roughly two parameter-sized buffers to training memory.
arXiv Machine Learning
Jul 8
Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning
arXiv:2607. 06151v1 Announce Type: new Abstract: Generalization remains a pivotal challenge in deep learning, where traditional optimizers like Stochastic Gradient Descent (SGD) often converge to sharp minima, leading to overfitting and reduced performance on unseen data.
arXiv Machine Learning
Jul 27
Hidden Failure Modes of Gradient Modification under Adam in Continual Learning, and Adaptive Decoupled Moment Routing as a Repair
arXiv:2604. 22407v2 Announce Type: replace Abstract: Many continual-learning methods modify gradients upstream (e.
arXiv AI
Jul 21
Singularity-aware Optimization via Randomized Geometric Probing: Towards Stable Non-smooth Optimization
arXiv:2605. 29547v2 Announce Type: replace-cross Abstract: Deep learning optimization relies heavily on the assumption of smooth loss landscapes, a condition systematically violated by modern architectures due to non-smooth components such as ReLU activations and quantization operators.
arXiv Machine Learning
1d ago
Adaptive Optimization via Momentum on Variance-Normalized Gradients
arXiv:2602. 10204v2 Announce Type: replace Abstract: We introduce MVN-Grad (Momentum on Variance-Normalized Gradients), an Adam-style optimizer that improves stability and performance by combining two complementary ideas: variance-based normalization and momentum applied after normalization.
arXiv Machine Learning
Aug 11
Gradient Under Microscope: Benchmarking Resource Utilization of Memory-Efficient Gradient Computation Methods
arXiv:2608. 08961v1 Announce Type: new Abstract: AI training's rising resource intensity is straining electricity supplies and carbon budgets, motivating systematic study of memory-efficient training on constrained hardware.