@natolambert: New podcast with @finbarrtimbers! We survey the latest post-training recipes, from GLM 5.1, Kimi K2.6, DeepSeek V4, Xia…
Summary
Nathan Lambert and Finbarr Timbers discuss the latest post-training recipes for large language models, including DeepSeek V4, GLM 5.1, Kimi K2.6, and the industry shift to multi-teacher on-policy distillation.
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New podcast with @finbarrtimbers! We survey the latest post-training recipes, from GLM 5.1, Kimi K2.6, DeepSeek V4, Xiaomi MiMo V2.5, Nemotron Ultra, etc. and discuss:
- Why the industry slowly shifted to multi-teacher on-policy distillation (MOPD).
- What an Olmo-style recipe would need improvements in
- How post-training works / suits larger organizational efforts
- Career advice in the foothills of the singularity
- and other topics
I heard y’all wanted me to start doing this, so making some time when I’m in funemployment!
Chapters:
00:00 Introduction & Olmo reflections 06:28 Post-train recipes review (history) 23:00 2026’s model recipes (MiMo Flash, DeepSeek V4, GLM 5, Kimi K2.6, etc.) 39:05 Open-ended post-training discussions 48:22 Career advice in the LLM race
Links below, please follow @interconnectsai and like and subscribe and buy my book?
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