Pushing Model Limits
Tatsu discusses the trade-off between fine-tuning costs and data efficiency, emphasizing the need for substantial resources to push model boundaries. Patrick and Tatsu explore the potential for further optimizations through reinforcement learning mechanisms in model training.In this clip
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Unsupervised Learning
Ep 11: Stanford Professor Tatsu Hashimoto on AI Biases and Improving LLM Performance
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