elicit/machine-learning-list is ranked 10,230 among these 8 peers; m2dsupsdlclass/lectures-labs is ranked 10,231.

Elicit Machine Learning Reading List Purpose The purpose of this curriculum is to help new Elicit employees learn background in machine learning, with a focus on language models. I’ve tried to strike a balance between papers that are relevant for deploying ML in production and techniques that matter for longer-term scalability. If you don’t work at Elicit yet - we’re hiring ML and software engineers. How to read Recommended reading order: 1. Etc ✨ = Added after 2025/11

/26 Table of contents Fundamentals Introduction to machine learning Transformers Key foundation model architectures Training and finetuning Reasoning and runtime strategies In-context reasoning Task decomposition Debate Tool use and scaffolding Honesty, factuality, and epistemics Applications Science Forecasting Search and ranking ML in practice Production deployment Benchmarks Datasets Advanced topics World models and causality Planning Uncertainty, calibration, and active learning Interpretability and model editing Reinforcement learning The big picture AI scaling AI safety Economic and social impacts Philosophy Maintainer Fundamentals

Introduction to machine learning **Tier 1** A short introduction to machine learning But what is a neural network? Gradient descent, how neural networks learn **Tier 2** An intuitive understanding of backpropagation What is backpropagation really doing? An introduction to deep reinforcement learning **Tier 3** The spelled-out intro to neural networks and backpropagation: building micrograd (Karpathy) Backpropagation calculus Transformers **Tier 1** ✨ Intro to Large Language Models (Karpathy) But what is a GPT? Visual intro to transformers Attention in transformers, visually

explained Attention? The Illustrated Transformer **Tier 2** ✨ Deep Dive into LLMs like ChatGPT (Karpathy) Let's build the GPT Tokenizer (Karpathy) The Illustrated GPT-2 (Visualizing Transformer Language Models) Neural Machine Translation by Jointly Learning to Align and Translate Attention Is All You Need **Tier 3** ✨ The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A" The Annotated Transformer TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second Grokking: Generalization Beyond Overfitting on

Small Algorithmic Datasets A Mathematical Framework for Transformer Circuits Tier 4+ A Practical Survey on Faster and Lighter Transformers Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks Memorizing Transformers Transformer Feed-Forward Layers Are Key-Value Memories Key foundation model architectures **Tier 1** Language Models are Unsupervised Multitask Learners (GPT-2) Language Models are Few-Shot Learners (GPT-3) **Tier 2** ✨ DeepSeek-R1 (DeepSeek-R1) ✨ DeepSeek-V3 Technical Report (DeepSeek-V3) ✨ The Llama 3 Herd of Models

(Llama 3) LLaMA: Open and Efficient Foundation Language Models (LLaMA) Training language models to follow instructions with human feedback (OpenAI Instruct) **Tier 3** ✨ LLaMA 2: Open Foundation and Fine-Tuned Chat Models (LLaMA 2) ✨ Qwen2. 5) ✨ Titans: Learning to Memorize at Test Time ✨ Byte Latent Transformer ✨ Phi-4 Technical Report (phi-4) Tier 4+ Evaluating Large Language Models Trained on Code (OpenAI Codex) Mistral 7B (Mistral) Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5

) Gemini: A Family of Highly Capable Multimodal Models (Gemini) Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Mamba) Scaling