Nathan Lambert
Author of Interconnects; post-training researcher at Ai2
Machine learning researcher at the Allen Institute for AI working on open post-training recipes, and author of the Interconnects newsletter and the freely readable RLHF Book.
Key arguments & positions
- Argues open post-training recipes — not just open weights — are what make model behavior auditable.
- Writes that RLHF and its successors are better understood as product-shaping tools than as alignment solutions.
- Tracks the open-versus-closed model gap closely and publishes his working estimates of it.
Accomplishments
- Post-training lead on the Allen Institute for AI's open OLMo and Tulu model releases; previously at Hugging Face.
- Author of the freely readable RLHF Book.
- Writes Interconnects, widely read among practitioners for its coverage of training method details.
Papers & key writings
Links
In the library
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A plain-language explainer on RLCD, a way of aligning language models by learning from contrasting outputs rather than human ratings alone.
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