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AI Models: Post-Training Recipes and Future Trends Explored

By PulseAugur Editorial · [1 sources] ·

A new podcast episode features Nathan Lambert and Finbarr Timbers discussing recent advancements in AI model post-training techniques. The conversation covers the industry's shift towards multi-teacher on-policy distillation, the application of Olmo-style recipes, and the broader implications of post-training for large-scale AI efforts. The episode also touches on career advice within the rapidly evolving AI landscape, reviewing models like GLM 5.1, Kimi K2.6, DeepSeek V4, Xiaomi MiMo V2.5, and Nemotron Ultra. AI

IMPACT Provides insights into current AI model training methodologies and future trends, relevant for AI researchers and developers.

RANK_REASON Podcast discussing AI model training techniques and career advice.

Read on X — Nathan Lambert (Interconnects) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI Models: Post-Training Recipes and Future Trends Explored

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Podcast discussing AI model training techniques and career advice.
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model release, opinion
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102 days old
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COVERAGE [1]

  1. X — Nathan Lambert (Interconnects) TIER_1 English(EN) · natolambert ·

    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 d

    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 ht…

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