Nathan Lambert

Post-training researcher at Ai2, writes Interconnects

A LLN - large language Nathan - (RL, RLHF, society, robotics), athlete, yogi, chef Writes http://interconnects.ai At Ai2 via HuggingFace, Berkeley, and normal places

Articles & links

The geopolitical angle of the AI race is only going to keep accelerating. More domestic talent travel restrictions from the Chinese government. I expect more and more things like this, AI is getting embedded in the core of existing power structures. www.bloomberg.com/news/arti…

bloomberg.com
AI Weekly's analysis
  • China now requires government approval for overseas travel by top AI researchers at private firms including Alibaba and DeepSeek.
  • Travel restrictions were quietly applied to some DeepSeek executives in December 2025 before expanding to the broader private AI sector.
  • Beijing's extension of talent controls into private firms reflects how strategically sensitive it considers frontier AI development.
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Some ideas for what comes next, May 2026 Gemini Flash 3.5, Mythos, open-closed balance, America's open-source surge, emerging power struggles and more. www.interconnects.ai/p/some-ideas...

interconnects.ai
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Latest open artifacts (#21): Open model bonanza! Gemma 4, DeepSeek V4, Kimi K2.6, MiMo 2.5, GLM-5.1 & others. On CAISI's V4 assessment. An eventful month with one flagship release after another www.interconnects.ai/p/latest-ope...

interconnects.ai
AI Weekly's analysis
  • DeepSeek-V4-Flash ranked best-in-class for local agentic coding tasks across the full May open-model cohort benchmarked by Lambert.
  • Poolside's Laguna XS.2, a 33B MoE model under Apache 2.0, is the strongest open-weight release at its size class in this cohort.
  • The open-to-closed capability gap stands at 3-7 months and has been narrowing since DeepSeek R1 launched, per CASI data.
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Recent commentary

Being out of SF has lowered my information proximity but with the big upside of giving me space to cultivate my own beliefs and values around ai. We need more people zagging in AI, the monoculture just helps the incumbents win at this point.

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On-policy distillation is on track to be a lasting method in post-training. The list of areas would be: Instruction tuning (SFT/IFT) RLHF Direct Preference Optimization (DPO et al) RLVR On-policy Distillation (OPD) New classes of methods are rare! Excited to play.

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