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22min chapter
Nathan Lambert on the rise of "thinking" language models
AI Summer
Evolving Methods in Language Model Training
This chapter explores the advancements in language model training methodologies, shifting from pre-training to innovative post-training techniques. It examines the complexities of instruction tuning, preference fine-tuning, and the use of diverse training data sources to enhance model performance. The discussion highlights the impact of these evolving practices on user experience and model behavior, addressing the nuances that differentiate advanced models from traditional systems.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode