{"total":57,"rows":[{"id":"2411.04330","title":"Scaling Laws for Precision","created":"2024-11-07","cited_by_count":95,"area":null},{"id":"2406.19146","title":"Resolving Discrepancies in Compute-Optimal Scaling of Language Models","created":"2024-06-27","cited_by_count":88,"area":null},{"id":"2410.21676","title":"How Does Critical Batch Size Scale in Pre-training?","created":"2024-10-29","cited_by_count":73,"area":null},{"id":"2409.17858","title":"How Feature Learning Can Improve Neural Scaling Laws","created":"2024-09-26","cited_by_count":56,"area":null},{"id":"2503.02113","title":"Deep Learning is Not So Mysterious or Different","created":"2025-03-03","cited_by_count":56,"area":null},{"id":"2502.17578","title":"How Do Large Language Monkeys Get Their Power (Laws)?","created":"2025-02-24","cited_by_count":43,"area":"safety"},{"id":"2505.19227","title":"Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law","created":"2025-05-25","cited_by_count":24,"area":null},{"id":"2409.15156","title":"Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling","created":"2024-09-23","cited_by_count":23,"area":"lr"},{"id":"2602.07488","title":"Deriving Neural Scaling Laws from the statistics of natural language","created":"2026-02-07","cited_by_count":23,"area":null},{"id":"2503.12811","title":"A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules","created":"2025-03-17","cited_by_count":22,"area":null},{"id":"2505.07067","title":"Learning curves theory for hierarchically compositional data with power-law distributed features","created":"2025-05-11","cited_by_count":17,"area":null},{"id":"2510.01098","title":"Theory of Scaling Laws for In-Context Regression: Depth, Width, Context and Time","created":"2025-10-01","cited_by_count":16,"area":null},{"id":"2510.05197","title":"Efficient Prediction of Pass@k Scaling in Large Language Models","created":"2025-10-06","cited_by_count":16,"area":null},{"id":"2503.02877","title":"Weak-to-Strong Generalization Even in Random Feature Networks, Provably","created":"2025-03-04","cited_by_count":15,"area":null},{"id":"2601.10684","title":"On the origin of neural scaling laws: from random graphs to natural language","created":"2026-01-15","cited_by_count":14,"area":null},{"id":"2603.26554","title":"Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory","created":"2026-03-27","cited_by_count":14,"area":null},{"id":"2502.03009","title":"Scaling Laws for Upcycling Mixture-of-Experts Language Models","created":"2025-02-05","cited_by_count":13,"area":"moe"},{"id":"2506.08415","title":"Improved Scaling Laws in Linear Regression via Data Reuse","created":"2025-06-10","cited_by_count":11,"area":null},{"id":"2506.03470","title":"Models of Heavy-Tailed Mechanistic Universality","created":"2025-06-04","cited_by_count":10,"area":null},{"id":"2510.04780","title":"Kernel ridge regression under power-law data: spectrum and generalization","created":"2025-10-06","cited_by_count":10,"area":null},{"id":"2602.03685","title":"Universal One-third Time Scaling in Learning Peaked Distributions","created":"2026-02-03","cited_by_count":10,"area":null},{"id":"2602.03702","title":"Anytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging","created":"2026-02-03","cited_by_count":10,"area":null},{"id":"2605.29548","title":"Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention","created":"2026-05-28","cited_by_count":10,"area":null},{"id":"2410.09005","title":"Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectra","created":"2024-10-11","cited_by_count":9,"area":null},{"id":"2501.02423","title":"Scaling Laws for Floating Point Quantization Training","created":"2025-01-05","cited_by_count":8,"area":null},{"id":"2602.06797","title":"Optimal Learning Rate Schedules under Functional Scaling Laws: Power Decay and Warmup-Stable-Decay","created":"2026-02-06","cited_by_count":8,"area":null},{"id":"2502.05074","title":"Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models","created":"2025-02-07","cited_by_count":7,"area":null},{"id":"2602.04774","title":"Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model","created":"2026-02-04","cited_by_count":7,"area":null},{"id":"2601.22652","title":"Spectral Gradient Descent Mitigates Anisotropy-Driven Misalignment: A Case Study in Phase Retrieval","created":"2026-01-30","cited_by_count":6,"area":null},{"id":"2602.14208","title":"Fast Catch-Up, Late Switching: Optimal Batch Size Scheduling via Functional Scaling Laws","created":"2026-02-15","cited_by_count":6,"area":null},{"id":"2602.21472","title":"The Design Space of Tri-Modal Masked Diffusion Models","created":"2026-02-25","cited_by_count":6,"area":"diffusion"},{"id":"2606.25008","title":"Neural Scaling Universality: If Exponents Are Fixed, Time to Understand Coefficients","created":"2026-06-23","cited_by_count":5,"area":null},{"id":"2409.03734","title":"Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry","created":"2024-09-05","cited_by_count":4,"area":null},{"id":"2503.01314","title":"Scaling Law Phenomena Across Regression Paradigms: Multiple and Kernel Approaches","created":"2025-03-03","cited_by_count":4,"area":null},{"id":"2603.14578","title":"Power-Law Spectrum of the Random Feature Model","created":"2026-03-15","cited_by_count":4,"area":null},{"id":"2511.18661","title":"Fast Escape, Slow Convergence: Learning Dynamics of Phase Retrieval under Power-Law Data","created":"2025-11-24","cited_by_count":3,"area":null},{"id":"2512.24503","title":"Can Small Training Runs Reliably Guide Data Curation? Rethinking Proxy-Model Practice","created":"2025-12-30","cited_by_count":3,"area":"lr"},{"id":"2605.22341","title":"A Boundary-Layer Mechanism for One-Third Scaling in Online Softmax Classification","created":"2026-05-21","cited_by_count":3,"area":null},{"id":"2509.23963","title":"Evaluating the Robustness of Chinchilla Compute-Optimal Scaling","created":"2025-09-28","cited_by_count":2,"area":null},{"id":"2510.06419","title":"Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting","created":"2025-10-07","cited_by_count":2,"area":null},{"id":"2512.22088","title":"Unifying Learning Dynamics and Generalization in Transformers Scaling Law","created":"2025-12-26","cited_by_count":2,"area":null},{"id":"2608.14071","title":"Scaling Domain Data Repetition in LLM Pretraining","created":"2026-08-14","cited_by_count":2,"area":null},{"id":"2502.14123","title":"Understanding SGD with Exponential Moving Average: A Case Study in Linear Regression","created":"2025-02-19","cited_by_count":1,"area":"diffusion"},{"id":"2509.10000","title":"Neural Scaling Laws for Deep Regression","created":"2025-09-12","cited_by_count":1,"area":null},{"id":"2510.16322","title":"Memorizing Long-tail Data Can Help Generalization Through Composition","created":"2025-10-18","cited_by_count":1,"area":null},{"id":"2605.23591","title":"Asymmetric Scaling Laws from Sparse Features","created":"2026-05-22","cited_by_count":1,"area":null},{"id":"2605.31244","title":"Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail","created":"2026-05-29","cited_by_count":1,"area":null},{"id":"2606.08167","title":"Explaining Data Mixing Scaling Laws","created":"2026-06-06","cited_by_count":1,"area":null},{"id":"2509.10337","title":"Exact Generalisation Error Exposes Benchmarks Skew Graph Neural Networks Success (or Failure)","created":"2025-09-12","cited_by_count":0,"area":null},{"id":"2605.08578","title":"Probing the Impact of Scale on Data-Efficient, Generalist Transformer World Models for Atari","created":"2026-05-09","cited_by_count":0,"area":"wm"},{"id":"2605.10395","title":"Sharp feature-learning transitions and Bayes-optimal neural scaling laws in extensive-width networks","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2608.01032","title":"The Fourth Quadrant: A Stylized View of Benign Misfitting","created":"2026-08-02","cited_by_count":0,"area":null},{"id":"2608.22953","title":"Stochastic gradient descent with initial regularization","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2608.28442","title":"Curvature-Conditioned Multiscale Momentum with Sphere Constraints for LLM Pretraining","created":"2026-08-28","cited_by_count":0,"area":null},{"id":"2609.27964","title":"Linear RNN Scaling Laws: When Longer Sequences Beat More Sequences","created":"2026-08-23","cited_by_count":0,"area":null},{"id":"2609.32100","title":"Emergent One-Third Scaling Law as Attention Tries to Concentrate","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2610.16518","title":"Asymptotic Risk of Learning-Rate Schedules for Least-Squares Regression with Discretely Distributed Features","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}