Arcee AI Lands Open-Source AI's Most Vocal Advocate to Back Trinity

Arcee AI, the scrappy San Francisco startup that bet half its lifetime funding on a single open-weight model, just added one of the most credible voices in open-source AI to its roster. Nathan Lambert is joining Arcee as a Research Advisor, a move that pairs a researcher with deep post-training expertise with a company that has staked its entire identity on building truly open American AI.

Who Is Nathan Lambert?

Lambert was a Senior Research Scientist and post-training lead at the Allen Institute for AI (Ai2), where he led work on TULU, one of the few fully open post-training pipelines for language models. Post-training is the phase that happens after a model is pre-trained on raw text: it is where models are fine-tuned, aligned to human preferences, and taught to follow instructions using techniques like RLHF (reinforcement learning from human feedback) and RLVR (reinforcement learning from verifiable rewards, where the model is rewarded for producing answers that can be checked for correctness, like math or code).

The last 2.5+ years building OLMo, Tulu, and other projects will be one of the peaks of his entire career, Lambert wrote upon departing Ai2. Before that, he built the RLHF research team at Hugging Face and contributed reinforcement learning integrations to the widely-used Diffusers library. He holds a Ph.D. from UC Berkeley, where he worked at the intersection of robotics, model-based reinforcement learning, and control, with internships at Facebook AI and DeepMind.

Lambert is not just a researcher. He is also the author of The RLHF Book, the definitive reference on reinforcement learning from human feedback, and the founder of the Interconnects AI newsletter. He has 8,000 citations on Google Scholar and writes articles on AI research that are viewed millions of times annually. He appeared on the Lex Fridman podcast twice, and his work has won awards including the Best Theme Paper Award at ACL.