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.
Our 163rd episode with a summary and discussion of last week's big AI news!
Note: apology for this one coming out a few days late, got delayed in editing it -Andrey
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