A lot of people haved asked how they can learn more about modern ML and LLMs, so I thought I'd compile an (incomplete) selection of papers for getting started. 

1. Antebellum

• NNLM (Bengio 2003, UMontréal) - A Neural Probabilistic Language Model 
• Linguistic Regularities (Mikolov 2013, Microsoft Research) - Linguistic Regularities in Continuous Space Word Representations 
• Word2Vec (Mikolov 2013, Microsoft Research) - Efficient Estimation of Word Representations in Vector Space 
• Word2Vec (Mikolov 2013, Microsoft Research) - Distributed Representations of Words and Phrases and their Compositionality 
• GloVe (Pennington 2014, Stanford NLP) - GloVe: Global Vectors for Word Representation 
• ELMo (Peters 2018, UWash) - Deep Contextualized Word Representations 

2. Early Transformers

• Transformers (Vaswani 2017, Google) - Attention Is All You Need 
• GPT1 (Radford 2018, OpenAI) - Improving Language Understanding by Generative Pre-Training 
• BERT (Devlin 2018, Google) - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding 
• GPT2 (Radford 2019, OpenAI) - Language Models are Unsupervised Multitask Learners 
• RoBERTa (Liu 2019, FAIR) - RoBERTa: A Robustly Optimized BERT Pretraining Approach 
• ALBERT (Lan 2019, Google) - ALBERT: A Lite BERT for Self-supervised Learning of Language Representation 
• DistilBERT (Sanh 2019, Hugging Face) - DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter 
• | Distilation (Hinton 2015, Google) - Distilling the Knowledge in a Neural Network 
• Sparse models (Gupta 2017, Google) - To prune, or not to prune: exploring the efficacy of pruning for model compression 
• | Optimal Brain Damage (Le Cun 1989, AT&T Bell Labs) - Optimal Brain Damage 
• | Magnitude Pruning (Han 2015, Stanford, NVIDIA) - Learning both Weights and Connections for Efficient Neural Networks 

3. Scaling

^{3.0 Scaling Theory}

• Lottery hypothesis (Frankle 2018, MIT) - The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks 
• Double descent (Nakkiran 2019, Harvard, OpenAI) - Deep Double Descent: Where Bigger Models and More Data Hurt 
• Groking (Power 2022, OpenAI) - Grokking: Generalization Beyond Overfitting On Small Algorithmic Datasets 
• Scaling Laws (Kaplan 2020, OpenAI) - Scaling Laws for Neural Language Models 
• mu-p (Yang 2022, Microsoft) - Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer 
• | The Practitioner's Guide to the Maximal Update Parameterization 
• | Some Math behind Neural Tangent Kernel 
• Chinchilla (Hoffmann 2022, DeepMind) - Training Compute-Optimal Large Language Model 
• How to Scale 

^{3.1 Getting Big}

• T5 (Raffel 2019, Google) - Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer 
• GPT3 (Brown 2020, OpenAI) - Language Models are Few-Shot Learners 
• | Locally Banded Sparse Attention (Child 2019, OpenAI) - Generating Long Sequences with Sparse Transformers 
• | Gradient Noise Scale (McCandlish 2018, OpenAI) - An Empirical Model of Large-Batch Training 
• Codex (Chen 2019, OpenAI) - Evaluating Large Language Models Trained on Code 

^{3.2 Approximate Attention Graveyard}

• Transformer-XL (Dai 2019) - Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context 
• | Effective Receptive Field (Khandelwal 2018) 
• | Character-Level Transformers (Al-Rfou 2018) 
• Longformer (Beltagy 2020, AI2) - Longformer: The Long-Document Transformer 
• Reformer (Kitaev 2020, Google) - Reformer: The Efficient Transformer 
• BigBird (Zaheer 2020, Google) - Big Bird: Transformers for Longer Sequences 
• Performer (Choromanski 2021, Google) - Rethinking Attention with Performers 

^{3.3 Mixture of Experts}

• MoE (Shazeer 2017, Google) - Outrageously Large Neural Networks: The Sparsely-Gated Mixture-Of-Experts Layer 
• GShard (Lepikhin 2020, Google) - GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding 
• Switch Transformer (Fedus 2021, Google) - Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity 

4. Alignment, RL, Reasoning

^{4.1 Traditional RL}

• MDP framework (Bellman 1957) - Markov Decision Processes 
• Bellman equations (Bellman 1957) - Dynamic Programming 
• Q-learning (Watkins 1989) - Learning from Delayed Rewards 
• DQN (Mnih 2013, Deepmind) - Playing Atari with Deep Reinforcement Learning 
• REINFORCE (Williams 1992) - Simple Statistical Gradient-Following Algorithms 
• Actor-Critic (Konda 2000, MIT) - Actor-Critic Algorithms 
• A3C (Mnih 2016, Google) - Asynchronous Methods for Deep Reinforcement Learning 
• A2C (Wu 2017, OpenAI) - Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation 
• PPO (Schulman 2017, OpenAI) - Proximal Policy Optimization Algorithms 
• SAC (Haarnoja 2018, UC Berkeley) - Soft Actor-Critic 
• AlphaStar (Vinyals 2019, Google) - Grandmaster level in StarCraft II using multi-agent reinforcement learning 

^{4.2 Modern RL}

• LoRA (Hu 2019, Microsoft & CMU) - LoRA: Low-Rank Adaptation of Large Language Models 
• RLHF (InstructGPT) (Ouyang 2022, OpenAI) - Training language models to follow instructions with human feedback 
• DPO (Rafailov 2023, Stanford) - Direct Preference Optimization: Your Language Model is Secretly a Reward Model 
• Let's verify step by step (Lightman 2023, OpenAI) - Let's Verify Step by Step 
• GRPO (DeepSeekMath) (Shao 2024, DeepSeek) - DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models 
• The Art of Scaling Reinforcement Learning Compute for LLMs (Khatri 2025, Meta) 

^{4.3 Efficient RL}

• APRIL (Zhou 2025, CMU) - APRIL: Active Partial Rollouts in Reinforcement Learning to Tame Long-tail Generation 
• PODS (Xu 2025, CMU) - Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning 

^{4.4 Neo RL}

• SPICE (Liu 2025, Meta) - SPICE: Self-Play In Corpus Environments Improves Reasoning 
• SPIRAL (Liu 2025) - SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning 
• Absolute Zero (Zhao 2025, Tsinghua) - Absolute Zero: Reinforced Self-play Reasoning with Zero Data 

5. Agentic, Inference-time compute

^Thinking

• Chain-of-Thought (Wei 2022, Google) - Chain-of-Thought Prompting Elicits Reasoning in Large Language Models 
• Scratchpad (Nye 2021, MIT & Google) - Show Your Work: Scratchpads for Intermediate Computation with Language Models 
• Let's think step by step (Kojima 2022, Google) - Large Language Models are Zero-Shot Reasoners 
• CoCoNut (Hao 2024, Meta) - Training Language Models to Reason in a Latent Space 
• STAR (Zelikman 2022, Stanford) - STaR: Bootstrapping Reasoning With Reasoning 
• RLVR (DeepSeek-R1) (Guo 2025, DeepSeek) - DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning 

^Agents

• RAG (Lewis 2020, Meta) - Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks 
• Toolformer (Schick 2023, Meta) - Toolformer: Language Models Can Teach Themselves to Use Tools 
• Agent Swarm (KimiK2.5) (Bai 2025, Moonshot AI) - Kimi K2.5: Visual Agentic Intelligence 

6. Research Revival

• Engram (Cheng 2025, Deepseek) - Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models 
• Attention Residuals (Chen 2026, Moonshot AI) - Attention Residuals 
• | mHC: Manifold-Constrained Hyper-Connections (Xie 2025, Deepseek) - mHC: Manifold-Constrained Hyper-Connections 

ML Systems

• FlashAttention (Dao 2022, Stanford) - FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness 
• | CUDA Programming Guide (NVIDIA) 
• | Learning CUDA by optimizing softmax (Pandya 2025) 
• | Basic idea behind flash attention 
• | Making Deep Learning Go Brrrr From First Principles (He 2022) 
• | What Shapes Do Matrix Multiplications Like? (He 2024) 
• | NVIDIA Deep Learning Performance (NVIDIA) 
• | Building Machine Learning Systems for a Trillion Trillion FLOPs (He 2024) 
• | Basic facts about GPUs 
• | Tips for Optimizing GPU Performance Using Tensor Cores (NVIDIA) 
• FlashAttention2 (Dao 2023, Stanford) - FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning 
• FlashAttention3 (Shah 2024, Stanford) - FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision 

^Quantization 

• BFloat16 (Wang 2019, Google) - BFloat16: The secret to high performance on Cloud TPUs 
• Microscaling Formats (Rouhani 2023, Microsoft & AMD & Intel & Meta & NVIDIA & Qualcomm) - Microscaling Data Formats for Deep Learning 
• LLM.int8() (Dettmers 2022, UWash & Meta & HuggingFace) - LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale 
• SmoothQuant (Xiao 2022, MIT & NVIDIA) - SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models 
• GPTQ (Frantar 2022, ETH Zurich) - GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers 
• LLM-QAT (Liu 2023, Meta) - LLM-QAT: Data-Free Quantization Aware Training for Large Language Models 
• AWQ (Lin 2023, Google) - Attacking Large Language Models with Projected Gradient Descent 
• QLoRA (Dettmers 2023, UWash) - QLoRA: Efficient Finetuning of Quantized LLMs 
• BitNet (Wang 2023, Microsoft) - BitNet: Scaling 1-bit Transformers for Large Language Models 
• 1.58 BitNet (Ma 2024, Microsoft) - The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits 
• Quantization Scaling Laws (Dettmers 2022, UWash) - The case for 4-bit precision: k-bit Inference Scaling Laws 
• KIVI (Liu 2024, CMU) - KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache 
• TurboQuant (Zandieh 2025, Google) - TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate 
• Introduction to 8-bit Matrix Multiplication for transformers - A Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale 

^KV-Cache

• Paged Attention / vLLM (Kwon 2023, Berkeley et al.) - Efficient Memory Management for Large Language Model Serving with PagedAttention 
• Deepseek-V2: Multi-Head Latent Attention (Liu 2024, DeepSeek) - DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model 
• | How Attention Got So Efficient 
• | Understanding Multi-Head Latent Attention 
• DeepSeek-V3.2: DSA Lightning Indexer (Liu 2025, DeepSeek) - DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models 

^Deployment

• Orca (Yu) - Orca: A Distributed Serving System for Transformer-Based Generative Models 
• Survey of efficient inference for LLMs (Zhou 2024) 
• Speculative Decoding (Leviathan 2022, Google) - Fast Inference from Transformers via Speculative Decoding 
• | Looking back on speculative decoding (Google) - Looking back on speculative decoding 
• Speculative speculative decoding (Kumar 2026, Google) - Speculative Speculative Decoding 

Vision / Multimodal

• Vision Transformers (ViT) (Dosovitskiy 2020, Google) - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale 
• Data-efficient Image Transformers (DeiT) (Touvron 2021, Meta) - Training data-efficient image transformers & distillation through attention 
• LLaVa (Liu 2023, Microsoft) - Visual Instruction Tuning 

Optimizers

• SGD (Robbins 1951) - A Stochastic Approximation Method 
• Momentum - Some methods of speeding up the convergence of iteration methods 
• AdaGrad (Duchi 2011) - Adaptive Subgradient Methods for Online Learning and Stochastic Optimization 
• RMSProp (Hinton 2012, Coursera) - rmsprop: Divide the Gradient by a Running Average of Its Recent Magnitude 
• Adam (Kingma 2015, OpenAI) - Adam: A Method for Stochastic Optimization 
• AdamW (Loshchilov 2019) - Decoupled Weight Decay Regularization 
• Shampoo (Gupta 2018, Google) - Shampoo: Preconditioned Stochastic Tensor Optimization 
• Lion (Chen 2023, Google) - Symbolic Discovery of Optimization Algorithms 
• Muon (Jordan 2024) - Muon: An Optimizer for Hidden Layers in Neural Networks 
• | Muon is Scalable for LLM Training 
• | Newton-Schulz - Iterative Newton-Schulz Orthogonalization 
• | Deriving Muon (Bernstein 2025) - Deriving Muon 
• | Whitening optimizer (Frans 2025, BAIR) - What really matters in matrix-whitening optimizers? 
• MuonClip (Kimi K2) (Bai 2025, Moonshot AI) - Kimi K2: Open Agentic Intelligence 

Footnotes

• Byte-pair tokenization (Sennrich 2016, UEdinburgh) - Neural Machine Translation of Rare Words with Subword Units 
• Unembedding Tying (Inan 2016, Stanford NLP) - Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling 
• Unembedding Tying (Press 2016, UTel-Aviv) - Using the Output Embedding to Improve Language Models 
• RoPE (Su 2021) - RoFormer: Enhanced Transformer with Rotary Position Embedding 
• | You could have designed state of the art positional encoding 
• Reasoning is fake (Pfau 2024, NYU) - Let's Think Dot by Dot: Hidden Computation in Transformer Language Models 
• Model Nondeterminism - Defeating Nondeterminism in LLM Inference 
• Streaming LLM Attention Sinks - Efficient Streaming Language Models with Attention Sinks 

^{Mechanistic Interpretability}

• Circuits (Ameisen 2025, Anthropic) - Circuit Tracing: Revealing Computational Graphs in Language Models 
• H-neurons (Gao 2025, Tsinghua) - H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs 
• Emotions (Sofroniew 2026, Anthropic) - Emotion Concepts and their Function in a Large Language Model