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Exploring Post-Training and Data in AI Development
Discussion on the debate surrounding post-training in AI models, its potential to enhance capabilities to near-AGI levels, and the importance of data, including 'frontier data' consisting of reasoning, code, math, and STEM data. Emphasis on overcoming the 'data wall' through various approaches like building expert datasets and using synthetic data. Additionally, exploring the challenges of synthetic data, implications of expert knowledge, and technical infrastructure in creating valuable datasets, as well as the risks of industrial espionage in stealing AI model weights.