In this episode of Artificial Intelligence: Papers and Concepts, curated by Dr. Satya Mallick, we break down DeepMind's 2022 paper "Training Compute-Optimal Large Language Models"—the work that challenged the "bigger is always better" era of LLM scaling.

You'll learn why many famous models were under-trained, what it means to be compute-optimal, and why the best performance comes from scaling model size and training data together.

We also unpack the Chinchilla vs. Gopher showdown, why Chinchilla won with the same compute budget, and what this shift means for the future: data quality and curation may matter more than ever.

Resources:

Paper : Training Compute-Optimal Large Language Models https://arxiv.org/pdf/2203.15556

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