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AI Summer
Nathan Lambert on the rise of "thinking" language models
Jan 14, 2025
Nathan Lambert, a research scientist and author of the AI newsletter Interconnects, dives into the fascinating world of language model evolution. He breaks down the shift from pre-training to innovative post-training techniques, emphasizing the complexities of instruction tuning and diverse data usage. Lambert discusses the advancements in reinforcement learning that enhance reasoning capabilities and the balance between scaling models and innovative techniques. He also touches on ethical considerations and the quest for artificial general intelligence amidst the growing field of AI.
01:00:49
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Quick takeaways
- The transition from pre-training to post-training signifies a new era in AI development, focusing on refining models for specific tasks.
- Techniques like instruction tuning and reinforcement learning from human feedback enhance AI capabilities, adapting them to better meet user needs.
Deep dives
Transition from Pre-training to Post-training Era
The discussion highlights the shift from the pre-training phase of AI models, characterized by extensive training on vast amounts of internet data, to the post-training era. Renowned ML researcher Ilya Sutskever asserts that the availability of new internet data has diminished, signaling an end to traditional pre-training. The focus has now turned to refining AI models after their initial training phase through post-training techniques. These are critical for developing specialized models that can effectively serve practical applications in various areas.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode