Training Compute-Optimal Large Language Models
New research shows that to get the most from a given compute budget, AI models should scale in size and data together. A smaller model trained on more data can outperform larger rivals while being more efficient.
Why it matters
This research provides a new framework for training large language models that can lead to faster, cheaper, and more widely deployable AI applications.
Key Points
- 1Larger AI models are not always better if they are undertrained on data
- 2Scaling model size and training data together is key to achieving optimal performance
- 3The 'Chinchilla' model demonstrates how a smaller network with more data can beat larger models
- 4This approach enables faster, cheaper AI that is more accessible to teams
Details
The article discusses new research findings that challenge the common assumption that simply increasing the size of AI models will lead to better performance. Many large language models have grown in size while using about the same amount of training data, leaving them undertrained. The results show that to get the most from a given compute budget, models should scale in size and data together - for every doubling of model size, the training data should also be doubled. This approach, exemplified by the 'Chinchilla' model, allows a smaller network trained on much more data to outperform several huge models while being more efficient and cost-effective to run. This flips the common idea that bigger is always better, and points the way forward for building the next generation of high-performing, accessible AI systems.
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