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Advancements in Model Training and Hardware Efficiency
The chapter discusses the complexities of training models, touching upon limitations of data compression and potential future advancements in model improvement and hardware efficiency. Debates include the necessity of models having world knowledge versus focusing solely on reasoning abilities, as well as creating models with extended context and reasoning abilities without retaining all factual information.