Nathan Lambert Portfolio
RLHF and post-training researcher who led development of the OLMo and Tulu open models as post-training lead at the Allen Institute for AI (AI2); writes the Interconnects newsletter and authored a freely available, structured "RLHF Book" alongside a Google Scholar record of peer-reviewed papers on reward modeling and reasoning.
What makes it work
A breakdown of the choices that make this portfolio stand out.
A full open textbook, not just blog posts
The RLHF Book compiles the mechanics of reinforcement learning from human feedback into one structured, freely accessible reference — a materially bigger undertaking than a series of standalone posts, and now a commonly cited starting point for the subfield.
Commentary paired with a podcast for primary-source interviews
Interconnects Interviews lets him bring other researchers on directly rather than only characterizing their work secondhand, adding a reporting layer on top of the analysis.
Willingness to write candidly about the AI job market and lab dynamics
Posts on hiring trends and how labs operate internally are riskier to publish than pure technical content, and that candor is part of what makes the newsletter widely read inside labs, not just by outside observers.
What AI Researchers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
If you understand a subfield deeply enough, write the textbook for it — a structured reference outlasts any single blog post.
Add a podcast or interview format alongside your writing so primary sources speak in their own words, not just through your summary.
Don't be afraid to write candidly about the state of your industry alongside pure technical content — it's part of what makes a research newsletter get read inside the field, not just outside it.
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