Iterative Training and Model Improvement
The hosts discuss the concept of features that are consistently useful under different learning conditions and explore the potential for developing algorithms that capture the same benefit without the need for an expensive iterative training process. They also delve into the idea of iterative refinement as a paradigm for improving models and how it has become more prevalent in the field of large language models.In this clip
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The Gradient
Hattie Zhou: Lottery Tickets and Algorithmic Reasoning in LLMs
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