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We present a smoothly broken power law functional form (that we refer to as a Broken Neural Scaling Law (BNSL)) that accurately models & extrapolates the scaling behaviors of deep neural networks (i.e.
A constructive prediction of the generalization error across scales
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
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Matplotlib: A 2d graphics environment
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Nature , 552(7683):63–66, nov 2017
Direct detection of a break in the teraelectronvolt cosmic-ray spectrum of electrons and positrons · 2017
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Deep Learning Scaling is Predictable, Empirically
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Proximal policy optimization algorithms
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
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Neural discrete representation learning
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Attention is all you need
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The narrativeqa reading comprehension challenge
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Towards deep learning models resistant to adversarial attacks
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Leveraging procedural generation to benchmark reinforcement learning
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Scaling Laws for Autoregressive Generative Modeling
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, and Sam McCandlish · 2020
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Scaling Laws for Neural Language Models
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Scipy 1.0: fundamental algorithms for scientific computing in python
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Intriguing properties of adversarial training at scale
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Exploring the limits of large scale pre-training, 2021
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Data scaling laws in nmt: The effect of noise and architecture
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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Improving language models by retrieving from trillions of tokens
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The case for 4-bit precision: k-bit inference scaling laws
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Explaining neural scaling laws
Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, and Utkarsh Sharma · 2021
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Truthful ai: Developing and governing ai that does not lie
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Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish · 2021
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Scaling scaling laws with board games
Andy L Jones · 2021
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Truthfulqa: Measuring how models mimic human falsehoods
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Deep double descent: Where bigger models and more data hurt
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X-risk analysis for ai research
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Robust speech recognition via large-scale weak supervision
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Effect of scale on catastrophic forgetting in neural networks
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Beyond neural scaling laws: beating power law scaling via data pruning, 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2022
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Colt5: Faster long-range transformers with conditional computation, 2023
Joshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontañón, Siddhartha Brahma, Yury Zemlyanskiy, David Uthus, Mandy Guo, James Lee-Thorp, Yi Tay, Yun-Hsuan Sung, and Sumit Sanghai · 2023
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Massive language models can be accurately pruned in one-shot
Elias Frantar and Dan Alistarh · 2023
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Scaling laws for single-agent reinforcement learning
Jacob Hilton, Jie Tang, and John Schulman · 2023
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Fair-ensemble: When fairness naturally emerges from deep ensembling
Wei-Yin Ko, Daniel D’souza, Karina Nguyen, Randall Balestriero, and Sara Hooker · 2023
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OpenAI · 2023
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Rwkv: Reinventing rnns for the transformer era
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Phasic policy gradient
Karl W Cobbe, Jacob Hilton, Oleg Klimov, and John Schulman · 2027
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