# Nathan Lambert

> Nathan Lambert is a machine learning researcher at the Allen Institute for AI working on open language models, RLHF, post-training, reinforcement learning, and robotics.

This file is a curated, LLM-readable map of natolambert.com. For a single-file context dump, use [llms-full.txt](https://natolambert.com/llms-full.txt).

## Core Pages
- [Homepage](https://natolambert.com/): Profile and links for Nathan Lambert.
- [CV](https://natolambert.com/cv): Curriculum vitae and publication record.
- [Contact](https://natolambert.com/contact): How to contact Nathan Lambert.
- [Writing](https://natolambert.com/writing): Local writing and Interconnects archive links.
- [Bookshelf](https://natolambert.com/bookshelf): Books Nathan Lambert has read and recommends.
- [Library](https://natolambert.com/slides): Talk slides, recordings, and presentations.

## Local Writing
- [Job Hunt as a PhD in AI / ML / RL: How it Actually Happens](https://natolambert.com/writing/ai-phd-job-hunt): The full breakdown of what a job search in AI with a new Ph.D. looks like.
- [The last reliable (available) path into AI](https://natolambert.com/writing/path-into-ai): A confluence of trends leaves AI+something, rather than pure AI, as the last great path into machine learning research.
- [ML/RL & Microrobotics](https://natolambert.com/writing/ml-rl-microrobotics): A memo I wrote to my research group on the open questions when applying machine learning to another research area: novel microrobotics.
- [Exploitation Exploration (in MBRL)](https://natolambert.com/writing/exploitation-exploration): A few lessons from model-based reinforcement learning how exploration can happen through exploitation of some metric.
- [Lifelong Learning 2021](https://natolambert.com/writing/lifelong-learning-2021): What I have been learning from recently.
- [Robot learning, model-based RL, and related optimization at NeurIPs 2020](https://natolambert.com/writing/neurips-2020): What I learned about deep RL and model-based learning at NeurIPs 2020.
- [Debugging Deep Model-based Reinforcement Learning Systems](https://natolambert.com/writing/debugging-mbrl): Things I have learned in 3 years of a young, and generally tricky research field.
- [All grad students (should) study graphic design](https://natolambert.com/writing/grad-students-graphic-design): People judge your papers by their cover. You can trick them into believing your science with pretty pictures.
- [Reflecting on being a graduate student (in AI) in 2020](https://natolambert.com/writing/reflecting-on-being-a-graduate-student-in-ai-in-2020): Starting to build my guide and advice for graduate school.
- [A Different Intro to RL in 30 Minutes](https://natolambert.com/writing/intro-to-rl-in-30-minutes): A 30 minute conceptual intro to Markov decision processes, iterative updates, and reinforcement learning.
- [Medium tries to save its writers](https://natolambert.com/writing/medium): Why Medium is not a website designed for the best writers.

## Guides
- [How to Review a Paper](https://natolambert.com/guides/how-to-review-a-paper): Academic paper review process.
- [Applying to Graduate School](https://natolambert.com/guides/grad-apps): Graduate school and fellowship application advice.

## External Context
- [Google Scholar](https://scholar.google.com/citations?hl=en&user=O4jW7BsAAAAJ&view_op=list_works&sortby=pubdate): Publication list.
- [Interconnects](https://www.interconnects.ai/): Newsletter and current AI writing.
- [RLHF Book](https://rlhfbook.com/): Book on reinforcement learning from human feedback and post-training.
- [Semantic Scholar](https://www.semanticscholar.org/author/Nathan-Lambert/2052363815): Publication profile.

## Optional
- [Full site context](https://natolambert.com/llms-full.txt): Concatenated text for the main pages, local posts, guides, bookshelf, and local slide metadata.
- [Sitemap](https://natolambert.com/sitemap.xml): XML sitemap for indexable pages.