Nathan Lambert
36 Intel
Nathan Lambert is a machine learning researcher who earned his PhD from UC Berkeley working at the intersection of machine learning and robotics. He spent over two years at the Allen Institute for AI (Ai2) as a senior research scientist and post-training lead, contributing to open language models including OLMo and the Tulu series, after earlier helping build the RLHF research team at Hugging Face; he left Ai2 in mid-2026. He writes the widely read AI newsletter Interconnects and authored a reference book on reinforcement learning from human feedback (RLHF).
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Intel
Nathan Lambert argues the open-model cyber-risk debate is stuck between frontier-lab and national-security alarm, Western defenders of open weights, and Chinese labs shipping cyber-capable open models. He contends that siloed bans fail to stop bad actors and leave everyone worse off.
A podcast interview and technical summary tracing how large language model post-training recipes have evolved from InstructGPT to 2026-era frontier models. It explains the shift toward Multi-teacher On-Policy Distillation (MOPD) and compares recipes across DeepSeek, Llama, Tülü, OLMo, MiMo, Nemotron, Kimi, and GLM.
Nathan Lambert assesses how Meta's Muse Spark Open, Reflection's Beam and Mistral's announced Large 4 reshape the open-weight landscape, roughly halving the gap between the best American and Chinese open models.
A firsthand essay from Interconnects AI documenting a trip to visit most of China's leading AI labs, exploring the cultural, organizational, and industry differences that shape how Chinese researchers build language models.
An analysis essay from the Interconnects AI newsletter arguing that closed frontier labs (OpenAI, Anthropic) and open model builders are on divergent economic trajectories, with closed models monetizing high-end knowledge work while open models diffuse broadly across the economy.
Nathan Lambert's congressional briefing notes assess the current US-China balance of power in open-weight models, explaining the open-source vs. open-weight distinction and why Chinese labs have led open releases since roughly April 2025.
Nathan Lambert argues that comparisons between the AI boom and past industrial revolutions overlook that ordinary people currently see few tangible benefits from AI, which slows real-world diffusion despite rapid capability progress.
a16z's Lisha Li and Nathan Lambert debate how durable the lead of frontier labs over open models is, covering distillation, compute limits, RL environments, post-training data, and Lambert's new nonprofit lab Trillium.
Nathan Lambert and Epoch AI's JS Denain debate predictions for recursive self-improvement, the true size of the US-China capability gap, whether distillation explains it, and what a frontier post-training recipe looks like today.
An analytical essay from the Interconnects AI newsletter examining why the perceived capability gap between open and closed language models is more nuanced than a single benchmark number suggests. It explores how benchmark relevance shifts over time, the role of RL environments and data in keeping fast-follower labs competitive, and the economic pressures driving frontier labs to constantly reinvent the 'frontier.'
A comprehensive book and free lecture series teaching post-training techniques for language models, from RLHF foundations to reinforcement learning algorithms. Aimed at helping practitioners transition from beginner to expert in modern LLM alignment methods.
Nathan Lambert on where AI writing actually threatens to do better: not high-voice creative work but the filler and copy text of non-fiction, and what that means for a textbook he wrote himself.