machine-learning-list

Why we included this project

Elicit built this reading list to onboard its own machine learning engineers, and the tiered structure shows it: newcomers start with fundamentals and work up to frontier-model research at a pace that makes sense. The selections deliberately balance papers that matter for shipping ML in production with ones that only pay off at longer time horizons, a trade-off you'd expect from a team actually deploying models. The scope is wide for a single syllabus, covering transformers and foundation-model architectures, reasoning and tool-use strategies, deployment, benchmarks, interpretability, reinforcement learning, and the safety and economic debates around scaling. For a developer or technical lead who wants an opinionated path instead of an undifferentiated link dump, the tier system and the explicit production-versus-research split make this a practical starting point.