arXiv Machine Learning
Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Sep 23
Practical Scaling Laws: Converting Compute into Performance in a Data-Constrained World
The paper introduces a new closed‑form scaling law that extends Chinchilla’s original formula to handle data‑constrained regimes. It decomposes loss into undercapacity, undertraining, and overfitting components, saturating between an irreducible loss and an uninformed baseline. The authors validate the model on diverse architectures and domains, achieving state‑of‑the‑art RMSE across multiple LLM scaling‑law grids and enabling cost‑aware training allocations.
arXiv AI
Jun 30
On the Nonlinearity of Learning Rate Scaling for LLM Training
arXiv:2606. 29158v1 Announce Type: cross Abstract: Learning-rate transfer can reduce the cost of training large language models: instead of sweeping learning rates at target scale, practitioners extrapolate from smaller runs.
arXiv AI
Jun 11
Unifying Learning Dynamics and Generalization in Transformers Scaling Law
arXiv:2512. 22088v3 Announce Type: replace-cross Abstract: The scaling law, a cornerstone of Large Language Model (LLM) development, predicts improvements in model performance with increasing computational resources.
arXiv AI
Jul 28
Hierarchical Grading in Large Language Models
arXiv:2607. 22757v1 Announce Type: cross Abstract: We introduce Graded Large Language Models (GLLMs), an algebraic framework that equips the representation space of a transformer with a grading and propagates the induced weighted scalar action through embeddings, self-attention, and the training objective.
arXiv Machine Learning
Sep 10
Feature Priming in Online Linear Regression: Sparse-Regret Lower Bounds and Tight Coordinatewise Rates
The paper investigates online linear regression with sparse comparators, focusing on feature priming techniques that reweight features using past data. It establishes sparse‑regret lower bounds that invalidate sparse‑logarithmic guarantees for univariate, Pearson, and multivariate priming rules under a past‑only Moore–Penrose protocol, showing ≥Ω(min{T,√d}) clipped regret for unit‑power rules and linear regret for powered rules in high dimensions. The authors also provide tight rank upper bounds for certain priming schemes and present algebraic constructions yielding Ω(min{T,d^{1/4}}) regret for unit‑power multivariate priming, while noting that the exact multivariate frontier remains open.
arXiv Machine Learning
Aug 11
The Cost of Adaptivity: Matching Lower Bounds Across Learning Problems
arXiv:2608. 08826v1 Announce Type: new Abstract: Adaptive procedures must work without nuisance information an oracle may use, such as a gradient scale or smoothness index, and robust procedures may have to answer queries whose coordinate and inspection time are chosen only after the data are seen.