Foundations of the Scaling Law
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2608.21653v1 Announce Type: cross Abstract: Multi-task learning often combines losses that span several orders of magnitude, causing homoscedastic uncertainty weighting to degrade severely. We...
Predictable Scaling (PS) is a framework that uses small‑scale training proxies to forecast full‑scale model performance, reducing the need for expensive GPU‑hourly runs. By applying composable scaling functions and curve fitting with uncertainty estimates, PS can rank candidate changes accurately across different hypothesis types. Experiments on two ranking models show PS achieves 79–88% compute savings while maintaining correct ranking of actionable hypotheses.
arXiv:2605. 31244v2 Announce Type: replace Abstract: Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute, and performance.
arXiv:2610.07324v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains. This setti...
arXiv:2608. 20061v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computational cost.
Existing machine learning (ML) scaling laws relate predictive loss to compute, model parameters, and data. However, as models are increasingly deployed through agentic harnesses, loss alone is insuffi...