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Scaling laws are typically fit using a family of models with a narrow range of frozen hyperparameter choices.
Huggingface’s transformers: State-of-the-art natural language processing, 2020
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 1910
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On the limited memory bfgs method for large scale optimization
Dong C. Liu and Jorge Nocedal · 1989
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2018
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Socialiqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al · 2020
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The pile: An 800gb dataset of diverse text for language modeling, 2020
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 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, et al · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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The depth-width interplay in self-attention
Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, and Amnon Shashua · 2020
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Scaling laws for neural machine translation
Behrooz Ghorbani, Orhan Firat, Markus Freitag, Ankur Bapna, Maxim Krikun, Xavier Garcia, Ciprian Chelba, and Colin Cherry · 2021
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Data and parameter scaling laws for neural machine translation
Mitchell A Gordon, Kevin Duh, and Jared Kaplan · 2021
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Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish · 2021
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2021
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Tuning large neural networks via zero-shot hyperparameter transfer
Ge Yang, Edward Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, and Jianfeng Gao · 2021
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Tensor programs iv: Feature learning in infinite-width neural networks
Greg Yang and Edward J. Hu · 2021
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Data scaling laws in nmt: The effect of noise and architecture
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, and Orhan Firat · 2022
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Wide attention is the way forward for transformers?
Jason Ross Brown, Yiren Zhao, Ilia Shumailov, and Robert D Mullins · 2022
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Unified scaling laws for routed language models
Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake Hechtman, Trevor Cai, Sebastian Borgeaud, et al · 2022
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An empirical analysis of compute-optimal large language model training
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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Axonn: An asynchronous, message-driven parallel framework for extreme-scale deep learning
Siddharth Singh and Abhinav Bhatele · 2022
Cited alongside, same era.
Scale efficiently: Insights from pretraining and finetuning transformers
Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, and Donald Metzler · 2022
Cited alongside, same era.
Examining scaling and transfer of language model architectures for machine translation
Biao Zhang, Behrooz Ghorbani, Ankur Bapna, Yong Cheng, Xavier Garcia, Jonathan Shen, and Orhan Firat · 2022
Cited alongside, same era.
Scaling laws for generative mixed-modal language models
Armen Aghajanyan, Lili Yu, Alexis Conneau, Wei-Ning Hsu, Karen Hambardzumyan, Susan Zhang, Stephen Roller, Naman Goyal, Omer Levy, and Luke Zettlemoyer · 2023
Cited alongside, same era.
Gqa: Training generalized multi-query transformer models from multi-head checkpoints
Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai · 2023
Scaling exponents across parameterizations and optimizers
Katie E Everett, Lechao Xiao, Mitchell Wortsman, Alexander A Alemi, Roman Novak, Peter J Liu, Izzeddin Gur, Jascha Sohl-Dickstein, Leslie Pack Kaelbling, Jaehoon Lee, and Jeffrey Pennington · 2024
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Language models scale reliably with over-training and on downstream tasks
Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, Suchin Gururangan, Mitchell Wortsman, Rulin Shao, Jean Mercat, Alex Fang, Jeffrey Li, Sedrick Keh, et al · 2024
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Olmo: Accelerating the science of language models
Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, and Hannaneh Hajishirzi · 2024
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The unreasonable ineffectiveness of the deeper layers
Andrey Gromov, Kushal Tirumala, Hassan Shapourian, Paolo Glorioso, and Daniel A Roberts · 2024
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Cited alongside, same era.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al · 2023
Cited alongside, same era.
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al · 2023
Cited alongside, same era.
Broken neural scaling laws
Ethan Caballero, Kshitij Gupta, Irina Rish, and David Krueger · 2023
Cited alongside, same era.
Cerebras-gpt: Open compute-optimal language models trained on the cerebras wafer-scale cluster
Nolan Dey, Gurpreet Gosal, Hemant Khachane, William Marshall, Ribhu Pathria, Marvin Tom, Joel Hestness, et al · 2023
Cited alongside, same era.
Scaling laws for sparsely-connected foundation models
Elias Frantar, Carlos Riquelme, Neil Houlsby, Dan Alistarh, and Utku Evci · 2023
Cited alongside, same era.
Width and depth limits commute in residual networks
Soufiane Hayou and Greg Yang · 2023
Cited alongside, same era.
Scaling data-constrained language models
Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin A Raffel · 2023
Cited alongside, same era.
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Scaling laws and compute-optimal training beyond fixed training durations
Alexander Hägele, Elie Bakouch, Atli Kosson, Loubna Ben Allal, Leandro Von Werra, and Martin Jaggi · 2024
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Minicpm: Unveiling the potential of small language models with scalable training strategies
Shengding Hu, Yuge Tu, Xu Han, Chaoqun He, Ganqu Cui, Xiang Long, Zhi Zheng, Yewei Fang, Yuxiang Huang, Weilin Zhao, et al · 2024
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Time matters: Scaling laws for any budget, June 2024
Itay Inbar and Luke Sernau · 2024
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Scaling laws for fine-grained mixture of experts
Jakub Krajewski, Jan Ludziejewski, Kamil Adamczewski, Maciej Pióro, Michał Krutul, Szymon Antoniak, Kamil Ciebiera, Krystian Król, Tomasz Odrzygóźdź, Piotr Sankowski, et al · 2024
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Scaling laws for diffusion transformers
Zhengyang Liang, Hao He, Ceyuan Yang, and Bo Dai · 2024
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Team OLMo, Pete Walsh, Luca Soldaini, Dirk Groeneveld, Kyle Lo, Shane Arora, Akshita Bhagia, Yuling Gu, Shengyi Huang, Matt Jordan, et al · 2024
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Reconciling kaplan and chinchilla scaling laws, 2024
Tim Pearce and Jinyeop Song · 2024
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The fineweb datasets: Decanting the web for the finest text data at scale
Guilherme Penedo, Hynek Kydlíček, Anton Lozhkov, Margaret Mitchell, Colin A Raffel, Leandro Von Werra, Thomas Wolf, et al · 2024
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The impact of depth on compositional generalization in transformer language models, 2024
Jackson Petty, Sjoerd van Steenkiste, Ishita Dasgupta, Fei Sha, Dan Garrette, and Tal Linzen · 2024
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Resolving discrepancies in compute-optimal scaling of language models
Tomer Porian, Mitchell Wortsman, Jenia Jitsev, Ludwig Schmidt, and Yair Carmon · 2024
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Observational scaling laws and the predictability of language model performance, 2024
Yangjun Ruan, Chris J. Maddison, and Tatsunori Hashimoto · 2024
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A 4d hybrid algorithm to scale parallel training to thousands of gpus, 2024
Siddharth Singh, Prajwal Singhania, Aditya K. Ranjan, Zack Sating, and Abhinav Bhatele · 2024
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Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk, David Atkinson, Russell Authur, Ben Bogin, Khyathi Chandu, Jennifer Dumas, Yanai Elazar, Valentin Hofmann, Ananya Harsh Jha, Sachin Kumar, Li Lucy, Xinxi Lyu, Nathan Lambert, Ian Magnusson, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind Tafjord, Pete Walsh, Luke Zettlemoyer, Noah A. Smith, Hannaneh Hajishirzi, Iz Beltagy, Dirk Groeneveld, Jesse Dodge, and Kyle Lo · 2024
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Toward inference-optimal mixture-of-expert large language models
Longfei Yun, Yonghao Zhuang, Yao Fu, Eric P Xing, and Hao Zhang · 2024
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When scaling meets llm finetuning: The effect of data, model and finetuning method
Biao Zhang, Zhongtao Liu, Colin Cherry, and Orhan Firat · 2024
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Don’t be lazy: Completep enables compute-efficient deep transformers, 2025
Nolan Dey, Bin Claire Zhang, Lorenzo Noci, Mufan Li, Blake Bordelon, Shane Bergsma, Cengiz Pehlevan, Boris Hanin, and Joel Hestness · 2025
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(mis)fitting scaling laws: A survey of scaling law fitting techniques in deep learning
Margaret Li, Sneha Kudugunta, and Luke Zettlemoyer · 2025
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Datadecide: How to predict best pretraining data with small experiments
Ian Magnusson, Nguyen Tai, Ben Bogin, David Heineman, Jena D Hwang, Luca Soldaini, Akshita Bhagia, Jiacheng Liu, Dirk Groeneveld, Oyvind Tafjord, et al · 2025
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Falcon-h1: A family of hybrid-head language models redefining efficiency and performance
Jingwei Zuo, Maksim Velikanov, Ilyas Chahed, Younes Belkada, Dhia Eddine Rhayem, Guillaume Kunsch, Hakim Hacid, Hamza Yous, Brahim Farhat, Ibrahim Khadraoui, et al · 2025
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