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b'Today we\\u2019re joined by Joseph Gonzalez, Assistant Professor in the EECS department at UC Berkeley. \\n\\nIn our conversation, we explore Joseph\\u2019s paper \\u201cTrain Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers,\\u201d which looks at compute-efficient training strategies for models. We discuss the two main problems being solved; 1) How can we rapidly iterate on variations in architecture? And 2) If we make models bigger, is it really improving any efficiency?'