• The Blessings of Scale: when more is different

• “Do Deep Convolutional Nets Really Need to be Deep and Convolutional?”, Urban et al 2016 (negative result, particularly on scaling—wrong, but why?)

• “Revisiting Unreasonable Effectiveness of Data in Deep Learning Era”, Sun et al 2017

• “Deep Learning Scaling is Predictable, Empirically”, Hestness et al 2017

• “Learning Visual Features from Large Weakly Supervised Data”, Joulin et al 2015; “Exploring the Limits of Weakly Supervised Pretraining”, Mahajan et al 2018; “Revisiting Weakly Supervised Pre-Training of Visual Perception Models”, Singh et al 2022 (CNNs scale to billions of hashtagged Instagram images)

• WebVision: “WebVision Challenge: Visual Learning and Understanding With Web Data”, Li et al 2017a/“WebVision Database: Visual Learning and Understanding from Web Data”, Li et al 2017b/“CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images”, Guo et al 2018

• “Measuring the Effects of Data Parallelism on Neural Network Training”, Shallue et al 2018

• “Gradient Noise Scale: An Empirical Model of Large-Batch Training”, McCandlish et al 2018

• “A Constructive Prediction of the Generalization Error Across Scales”, Rosenfeld et al 2019

• “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks”, Tan & Le2019

• “One Epoch Is All You Need”, Komatsuzaki2019

• “Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers”, Li et al 2020

• “Small Data, Big Decisions: Model Selection in the Small-Data Regime”, Bornschein et al 2020

• Key GPT papers:

  • “Scaling Laws for Neural Language Models”, Kaplan et al 2020

  • “Scaling Laws from the Data Manifold Dimension”, Sharma & Kaplan2020

  • “Scaling Laws for Autoregressive Generative Modeling”, Henighan et al 2020 (noise & resolution); “Broken Neural Scaling Laws”, Caballero et al 2022

  • “GPT-3: Language Models are Few-Shot Learners”, Brown et al 2020

  • “Measuring Massive Multitask Language Understanding”, Hendrycks et al 2020; “Measuring Mathematical Problem Solving With the MATH Dataset”, Hendrycks et al 2021

  • “Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers”, Hendricks et al 2021

  • “Scaling Laws for Transfer”, Hernandez et al 2021; “Scaling Laws for Language Transfer Learning”, Christina Kim (Hernandez et al 2021 followup: smooth scaling for En → De/Es/Zh); “When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method”, Zhang et al 2024

  • “Scaling Laws for Neural Machine Translation”, Ghorbani et al 2021; “Data and Parameter Scaling Laws for Neural Machine Translation”, Gordon et al 2021; “Unsupervised Neural Machine Translation with Generative Language Models Only”, Han et al 2021; “Data Scaling Laws in NMT: The Effect of Noise and Architecture”, Bansal et al 2022

  • “How Many Data Points is a Prompt Worth?”, Le Scao & Rush2021

  • “Recursively Summarizing Books with Human Feedback”, Wu et al 2021

  • “Codex: Evaluating Large Language Models Trained on Code”, Chen et al 2021 (small versions of GitHub Copilot, solves simple linear algebra/statistics problems too); “Program Synthesis with Large Language Models”, Austin et al 2021; “Show Your Work: Scratchpads for Intermediate Computation with Language Models”, Anonymous et al 2021; “Few-Shot Self-Rationalization with Natural Language Prompts”, Marasović et al 2021

  • “Scarecrow: A Framework for Scrutinizing Machine Text”, Dou et al 2021

  • “A Recipe For Arbitrary Text Style Transfer with Large Language Models”, Reif et al 2021

  • Instruction tuning/multi-task finetuning

  • “M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining”, Lin et al 2021

  • “Training Verifiers to Solve Math Word Problems”, Cobbe et al 2021

  • “Symbolic Knowledge Distillation: from General Language Models to Commonsense Models”, West et al 2021

  • “An Explanation of In-Context Learning as Implicit Bayesian Inference”, Xie et al 2021

• “Blender: Recipes for building an open-domain chatbot”, Roller et al 2020

• “Big Self-Supervised Models are Strong Semi-Supervised Learners”, Chen et al 2020a

• “iGPT: Generative Pretraining from Pixels”, Chen et al 2020b

• “GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding”, Lepikhin et al 2020; “Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity”, Fedus et al 2021; “Exploring Sparse Expert Models and Beyond”, Yang et al 2021

  • “On the Predictability of Pruning Across Scales”, Rosenfeld et al 2020 (scaling laws for sparsity: initially large size reductions are free, then power-law worsening, then plateau at tiny but bad models)

• “How big should my language model be?”, Huggingface2020

• “When Do You Need Billions of Words of Pretraining Data?”, Zhang et al 2020; “Learning Which Features Matter: RoBERTa Acquires a Preference forLinguistic Generalizations (Eventually)”, Warstadt et al 2020; “Probing Across Time: What Does RoBERTa Know and When?”, Liu et al 2021

• CLIP; “ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision”, Jia et al 2021 (see also CC-12M; EfficientNet trained on 1.8 billion images on a TPUv3-1024); “WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training”, Huo et al 2021; “Multimodal Few-Shot Learning with Frozen Language Models”, Tsimpoukelli et al 2021; “GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce”, Bell et al 2020; “Billion-Scale (Pinterest) Pretraining with Vision Transformers for Multi-Task Visual Representations”, Beale et al 2021

• “DALL·E 1: Zero-Shot Text-to-Image Generation”, Ramesh et al 2021 (blog); “M6: A Chinese Multimodal Pretrainer”, Lin et al 2021 (Chinese DALL·E 1: 1.9TB images/0.29TB text for 10b-parameter dense/100b-parameter MoE Transformer; shockingly fast Chinese replication of DALL·E 1/CLIP)

• “Improved Denoising Diffusion Probabilistic Models”, Nichol & Dhariwal2021 (DDPM scaling laws for FID & likelihood)

• “Automatic Curation of Large-Scale Datasets for Audio-Visual Representation Learning”, Lee et al 2021

• “Scaling Laws for Acoustic Models”, Droppo & Elibol2021

  • “XLSR: Unsupervised Cross-lingual Representation Learning for Speech Recognition”, Conneau et al 2020

  • “Scaling End-to-End Models for Large-Scale Multilingual ASR”, Li et al 2021; “Scaling ASR Improves Zero and Few Shot Learning”, Xiao et al 2021

  • “VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation”, Wang et al 2021; “wav2vec: Large-Scale Self-Supervised and Semi-Supervised Learning for Speech Translation”, Wang et al 2021 (fMRI); “XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale”, Babu et al 2021

  • “HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units”, Hsu et al 2021

  • Whisper

• “SEER: Self-supervised Pretraining of Visual Features in the Wild”, Goyal et al 2021; “Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision”, Goyal et al 2022

• “Fast and Accurate Model Scaling”, Dollár et al 2021; “Revisiting ResNets: Improved Training and Scaling Strategies”, Bello et al 2021

• “XLM-R: Unsupervised Cross-lingual Representation Learning at Scale”, Conneau et al 2019; “XLM-R XL/XLM-R XXL: Larger-Scale Transformers for Multilingual Masked Language Modeling”, Goyal et al 2021; “Facebook AI WMT21 News Translation Task Submission”, Tran et al 2021

• “ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation”, Sun et al 2021

• “LEMON: Scaling Up Vision-Language Pre-training for Image Captioning”, Hu et al 2021

• “Flamingo: a Visual Language Model for Few-Shot Learning”, Alayrac et al 2022

• “Scaling Vision Transformers”, Zhai et al 2021

• “CoAtNet: Marrying Convolution and Attention for All Data Sizes”, Dai et al 2021

• “BEiT: BERT Pre-Training of Image Transformers”, Bao et al 2021; “Masked Autoencoders Are Scalable Vision Learners”, He et al 2021

• “A Universal Law of Robustness via Isoperimetry”, Bubeck & Sellke2021; “Exploring the Limits of Out-of-Distribution Detection”, Fort et al 2021; “Partial success in closing the gap between human and machine vision”, Geirhos et al 2021

• “Effect of scale on catastrophic forgetting in neural networks”, Anonymous2021

• “On the Opportunities and Risks of Foundation Models”, Bommasani et al 2021 (review)

• “Exploring the Limits of Large Scale Pre-training”, Abnar et al 2021

• “Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers”, Prato et al 2021

• “E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials”, Batzner et al 2021

• Face recognition: “WebFace260M: A Benchmark for Million-Scale Deep Face Recognition”, Zhu et al 2022

• “Fine-tuned Language Models are Continual Learners”, Scialom at al 2022

• Embeddings: “DynamicEmbedding: Extending TensorFlow for Colossal-Scale Applications”, Zeng et al 2020; “DLRM: High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models”, Mudigere et al 2021; “Make Every feature Binary (MEB): A 135b-parameter sparse neural network for massively improved search relevance”; “Persia: A Hybrid System Scaling Deep Learning Based Recommenders up to 100 Trillion Parameters”, Lian et al 2021 (Kuaisho)

  • “Scaling Law for Recommendation Models: Towards General-purpose User Representations”, Shin et al 2021; “Understanding Scaling Laws for Recommendation Models”, Ardalani et al 2022

• MLPs/FCs: from the “Fully-Connected Neural Nets” bibliography: Urban et al 2016; “MLP-Mixer: An all-MLP Architecture for Vision”, Tolstikhin et al 2021; “gMLP: Pay Attention to MLPs”, Liu et al 2021

• Reinforcement Learning:

  • “Fine-Tuning Language Models from Human Preferences”, Ziegler et al 2019; “Learning to summarize from human feedback”, Stiennon et al 2020

  • “Measuring hardware overhang”, hippke (the curves cross: “with today’s [trained] algorithms, computers would have beat the world chess champion already in 1994_30ya on a contemporary desk computer”)

  • “Scaling Scaling Laws with Board Games”, Jones2021 (AlphaZero/Hex: highly-optimized GPU implementation enables showing smooth scaling across 6 OOM of compute—2× FLOPS = 66% victory; amortization of training → runtime tree-search, where 10× training = 15× runtime)

  • “MuZero Unplugged: Online and Offline Reinforcement Learning by Planning with a Learned Model”, Schrittwieser et al 2021

  • “From Motor Control to Team Play in Simulated Humanoid Football”, Liu et al 2021

  • “Open-Ended Learning Leads to Generally Capable Agents”, Open Ended Learning Team et al 2021; “Procedural Generalization by Planning with Self-Supervised World Models”, Anand et al 2021

  • “Fictitious Co-Play: Collaborating with Humans without Human Data”, Strouse et al 2021

  • “Gato: A Generalist Agent”, Reed et al 2022 (small Decision Transformer can learn >500 tasks; scaling smoothly)

  • “Multi-Game Decision Transformers”, Lee et al 2022 (near-human offline single-checkpoint ALE agent with scaling & rapid transfer)

• Theory:

  • “Does Learning Require Memorization? A Short Tale about a Long Tail”, Feldman2019

  • “Generalization bounds for deep learning”, Valle-Pérez & Louis2020

  • “The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers”, Nakkiran et al 2020

  • “Explaining Neural Scaling Laws”, Bahri et al 2021

  • “Learning Curve Theory”, Hutter2021 (Rohin Shah commentary; more on the manifold hypothesis)

  • “The Shape of Learning Curves: a Review”, Viering & Loog2021

  • “A mathematical theory of semantic development in deep neural networks”, Saxe et al 2019 (are jumps in NN capabilities to be expected when scaling? see also Viering & Loog2021’s discussion of phase transitions & averaging of exponentials giving power-laws, human “vocabulary spurts”, and “Acquisition of Chess Knowledge in AlphaZero”, McGrath et al 2021 §6 “Rapid increase of basic knowledge”); sequential learning in OpenFold

  • “A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning”, Dar et al 2021

• Historical:

  • “Toward A Universal Law Of Generalization For Psychological Science”, Shepard1987

  • “Scaling to Very Very Large Corpora for Natural Language Disambiguation”, Banko & Brill2001

  • “Large Scale Online Learning”, Bottou & LeCun 2003_21ya (“We argue that suitably designed online learning algorithms asymptotically outperform any batch learning algorithm.”)

  • “Tree Induction vs. Logistic Regression: A Learning-Curve Analysis”, Perlich et al 2003

  • “Large Language Models in Machine Translation”, Brants et al 2007_17ya; Koehn & Knowles2017 (Figure 3)

  • “The Unreasonable Effectiveness of Data”, Halevy et al 2009

  • “The Tradeoffs of Large-Scale Learning”, Bottou & Bousquet 2007_17ya/2012_12ya; “Large-Scale Machine Learning Revisited [slides]”, Bottou2013

• See Also: For more ML scaling research, follow the /r/MLScaling subreddit; “It Looks Like You’re Trying To Take Over The World”

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