The chapter explores a novel approach in training models by assigning varying levels of importance to tokens based on their difficulty. By optimizing training efficiency through a reference model, significant performance boosts are achieved, nearing GPT benchmarks. The focus is on token efficiency, mixture of experts, and challenging conventional models' training approaches.

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