This page shows the most relevant public items for Scaling Laws, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Blog • Feb 12, 2026 • Medium • Aqil Khan
As Big Tech burns $655 billion on power grids, we are hitting the 'Efficiency Wall.' Khan argues that the next 1,000x leap in AI capability will come from 'Agentic Efficiency' and specialized Small...
Paper • Oct 19, 2022 • arXiv • Leo Gao, John Schulman, Jacob Hilton
When optimizing a policy against a learned reward model, the policy eventually exploits errors in the reward model, leading to a decline in the true underlying objective. This phenomenon, known as ...
Paper • Oct 28, 2020 • arXiv • Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, Sam McCandlish
Building upon previous work establishing scaling laws for language models, we investigate whether similar power-law scaling relationships hold across other data modalities. We train autoregressive ...
Paper • Jan 23, 2020 • arXiv • Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, Dario Amodei
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, ...
Paper • Mar 29, 2022 • arXiv • Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Laurent Sifre
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly under...
Paper • Feb 12, 2026 • arXiv • Nicholas Lee, Lutfi Eren Erdogan, Chris Joseph John, Surya Krishnapillai, Kurt Keutzer, Amir Gholami
Current WebAgents struggle with long-horizon tasks and complex navigation. We propose an agentic scaling framework that increases compute at test-time through iterative trajectory pruning and rewar...
Paper • Dec 8, 2021 • arXiv • Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Eleni Elia, Danilo J. Rezende, Vinyals, Simonyan
Language modelling provides a step towards intelligent communication systems by harnessing large datasets and expressive models. We provide an analysis of Transformer-based language model architect...