Scale Weight Decay and Train Better | Paper Citation Record

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This paper cites Scaling Laws for Neural Language Models.

Scale Weight Decay and Train Better Scaling Laws for Neural Language Models

Reference 2

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This paper cites Training Compute-Optimal Large Language Models.

Scale Weight Decay and Train Better Training Compute-Optimal Large Language Models

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Scale Weight Decay and Train Better Explaining neural scaling laws

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Scale Weight Decay and Train Better An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

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Scale Weight Decay and Train Better Scaling Vision Transformers

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This paper cites Neural Scaling Laws in Robotics.

Scale Weight Decay and Train Better Neural Scaling Laws in Robotics

Reference 8

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This paper cites Kimi K2: Open Agentic Intelligence.

Scale Weight Decay and Train Better Kimi K2: Open Agentic Intelligence

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This paper cites GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models.

Scale Weight Decay and Train Better GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

Reference 10

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Scale Weight Decay and Train Better Unresolved cited work

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This paper cites A Simple Weight Decay Can Improve Generalization.

Scale Weight Decay and Train Better A Simple Weight Decay Can Improve Generalization

Reference 12

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This paper cites Why Do We Need Weight Decay in Modern Deep Learning?.

Scale Weight Decay and Train Better Why Do We Need Weight Decay in Modern Deep Learning?

Reference 13

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This paper cites Decoupled Weight Decay Regularization.

Scale Weight Decay and Train Better Decoupled Weight Decay Regularization

Reference 14

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This paper cites Why Warmup the Learning Rate? Underlying Mechanisms and Improvements.

Scale Weight Decay and Train Better Why Warmup the Learning Rate? Underlying Mechanisms and Improvements

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This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Scale Weight Decay and Train Better SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 16

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This paper cites 2024.url:https : //kellerjordan.github.io/posts/muon/.

Scale Weight Decay and Train Better 2024.url:https : //kellerjordan.github.io/posts/muon/

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Scale Weight Decay and Train Better Muon is Scalable for LLM Training

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Scale Weight Decay and Train Better A Stochastic Approximation Method

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Scale Weight Decay and Train Better Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

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This paper cites Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity.

Scale Weight Decay and Train Better Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Reference 21

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Scale Weight Decay and Train Better Why Gradients Rapidly Increase Near the End of Training

Reference 22

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Scale Weight Decay and Train Better Shampoo: Preconditioned Stochastic Tensor Optimization

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Scale Weight Decay and Train Better Scalable Second Order Optimization for Deep Learning

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Scale Weight Decay and Train Better SOAP: Improving and Stabilizing Shampoo using Adam

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Scale Weight Decay and Train Better Aurora: A Leverage-Aware Spectral Optimizer

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Scale Weight Decay and Train Better Dion: Distributed Orthonormalized Updates

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Scale Weight Decay and Train Better Unresolved cited work

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Scale Weight Decay and Train Better The Polar Express: Optimal Matrix Sign Methods and Their Application to the Muon Algorithm

Reference 29

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Scale Weight Decay and Train Better Anytime Training with Schedule-Free Spectral Optimization

Reference 30

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Scale Weight Decay and Train Better Unresolved cited work

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Scale Weight Decay and Train Better Muon$^2$: Boosting Muon via Adaptive Second-Moment Preconditioning

Reference 32

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Scale Weight Decay and Train Better Unresolved cited work

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Scale Weight Decay and Train Better Optimization Methods for Large-Scale Machine Learning

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Scale Weight Decay and Train Better Muown: Row-Norm Control for Muon Optimization

Reference 35

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Scale Weight Decay and Train Better Convergence Bound and Critical Batch Size of Muon Optimizer

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Scale Weight Decay and Train Better On the Convergence Analysis of Muon

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Scale Weight Decay and Train Better On the Convergence of Muon and Beyond

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Scale Weight Decay and Train Better Language Models are Unsupervised Multitask Learners

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Scale Weight Decay and Train Better Llama 2: Open Foundation and Fine-Tuned Chat Models

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Scale Weight Decay and Train Better Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

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Scale Weight Decay and Train Better Unresolved cited work

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Scale Weight Decay and Train Better Scaling Optimal LR Across Token Horizons

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Scale Weight Decay and Train Better L2 Regularization versus Batch and Weight Normalization

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Scale Weight Decay and Train Better Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks

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Scale Weight Decay and Train Better Unresolved cited work

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Scale Weight Decay and Train Better Unresolved cited work

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Scale Weight Decay and Train Better Mamba: Linear-Time Sequence Modeling with Selective State Spaces

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Scale Weight Decay and Train Better Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling

Reference 53

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Scale Weight Decay and Train Better Gated Delta Networks: Improving Mamba2 with Delta Rule

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Scale Weight Decay and Train Better Parallax: Parameterized Local Linear Attention for Language Modeling

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Scale Weight Decay and Train Better Training language models to follow instructions with human feedback

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Scale Weight Decay and Train Better Deep reinforcement learning from human preferences

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Scale Weight Decay and Train Better DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

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Scale Weight Decay and Train Better DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning

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Scale Weight Decay and Train Better Scaling Laws for Fine-Grained Mixture of Experts

Reference 61

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Scale Weight Decay and Train Better Direct Preference Optimization: Your Language Model is Secretly a Reward Model

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Scale Weight Decay and Train Better Inference economics of language models

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Scale Weight Decay and Train Better Root Mean Square Layer Normalization

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Scale Weight Decay and Train Better RoFormer: Enhanced Transformer with Rotary Position Embedding

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Scale Weight Decay and Train Better Benchmarking Optimizers for Large Language Model Pretraining

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Scale Weight Decay and Train Better A Spectral Condition for Feature Learning

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Scale Weight Decay and Train Better Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks

Reference 69

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Scale Weight Decay and Train Better GLU Variants Improve Transformer

Reference 70

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Scale Weight Decay and Train Better PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 71

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Scale Weight Decay and Train Better Unresolved cited work

Reference 74

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Scale Weight Decay and Train Better Rethinking Language Model Scaling under Transferable Hypersphere Optimization

Reference 75

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Observation ca591d46-0694-44a9-8a69-f1879540b5c5 · outbound

This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.

Scale Weight Decay and Train Better The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale

Reference 2024

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Scale Weight Decay and Train Better Unresolved cited work

Reference 2025

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