y0news
AnalyticsDigestsSourcesTopicsRSSAICrypto

#adam-optimizer News & Analysis

9 articles tagged with #adam-optimizer. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

9 articles
AIBullisharXiv – CS AI · May 277/10
🧠

Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers

Researchers introduce a symmetry-compatible principle for neural network optimizer design that aligns gradient updates with the geometric properties of different parameter types. The approach yields specialized update rules for embeddings, language model heads, SwiGLU MLPs, and mixture-of-experts routers, demonstrating improved validation loss and training stability across multiple language model architectures compared to standard AdamW optimization.

AIBullisharXiv – CS AI · Mar 37/104
🧠

A Convergence Analysis of Adaptive Optimizers under Floating-point Quantization

Researchers introduce the first theoretical framework analyzing convergence of adaptive optimizers like Adam and Muon under floating-point quantization in low-precision training. The study shows these algorithms maintain near full-precision performance when mantissa length scales logarithmically with iterations, with Muon proving more robust than Adam to quantization errors.

AINeutralarXiv – CS AI · May 126/10
🧠

Navigating LLM Valley: From AdamW to Memory-Efficient and Matrix-Based Optimizers

A comprehensive arXiv survey examines the evolution of optimization algorithms for large language model training, moving beyond Adam toward memory-efficient, second-order, and matrix-based approaches. The research emphasizes that modern LLM optimization requires rigorous, scale-aware benchmarking that evaluates convergence, stability, memory usage, and implementation complexity rather than isolated speedup claims.

AINeutralarXiv – CS AI · May 116/10
🧠

A Rod Flow Model for Adam at the Edge of Stability

Researchers extend rod flow modeling to Adam and other adaptive gradient methods, enabling more accurate continuous-time analysis of optimizer behavior at the edge of stability. This advancement bridges a gap in theoretical understanding of momentum-based optimization algorithms critical to modern deep learning.

AINeutralarXiv – CS AI · May 116/10
🧠

The Effect of Mini-Batch Noise on the Implicit Bias of Adam

Researchers present a theoretical framework showing how mini-batch noise in Adam optimizer training affects the implicit bias toward sharper or flatter loss landscape regions, finding that optimal momentum hyperparameters shift based on batch size—small batches favor the default (0.9, 0.999) settings while larger batches benefit from closer β₁ and β₂ values.

AIBullisharXiv – CS AI · May 96/10
🧠

Revealing Modular Gradient Noise Imbalance in LLMs: Calibrating Adam via Signal-to-Noise Ratio

Researchers present MoLS (Module-wise Learning Rate Scaling via SNR), a technique that automatically calibrates Adam optimizer updates across different modules in large language models by measuring signal-to-noise ratios. The method addresses optimization challenges caused by gradient heterogeneity across LLM components without requiring manual tuning, achieving performance comparable to hand-tuned approaches while maintaining compatibility with memory-efficient training.

AINeutralarXiv – CS AI · Mar 45/103
🧠

Why Adam Can Beat SGD: Second-Moment Normalization Yields Sharper Tails

Research paper establishes the first theoretical separation between Adam and SGD optimization algorithms, proving Adam achieves better high-probability convergence guarantees. The study provides mathematical backing for Adam's superior empirical performance through second-moment normalization analysis.