Summary of Scaling Law For Language Models Training Considering Batch Size, by Xian Shuai et al.
Scaling Law for Language Models Training Considering Batch Size
by Xian Shuai, Yiding Wang, Yimeng Wu, Xin Jiang, Xiaozhe Ren
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the effect of a critical hyperparameter, global batch size, on the training process of large language models (LLMs). The authors train models ranging from 125 million to 2.6 billion parameters using up to 300 billion high-quality tokens. They establish a scaling law relating model size and training data amount, then analyze how varying batch sizes and learning rates impact model convergence and generalization. The study yields two batch size scaling laws under different resource constraints: one for fixed compute budget and another for fixed training data amount. The authors validate their predicted laws through extrapolation experiments on larger models, providing guidance for optimizing LLM training strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding how to train language models better. Language models are like super smart computers that can understand and generate human-like text. The researchers did many experiments with different-sized language models using lots of text data. They found out that the amount of text data needed increases as the size of the model gets bigger. They also discovered that the way they divide up the training process (called batch size) affects how well the model learns and how good it is at generating new text. The study helps us understand how to optimize the training process for language models, which can be useful in many areas such as chatbots, search engines, and more. |
Keywords
» Artificial intelligence » Generalization » Hyperparameter » Scaling laws
