Researchers from CoAI Group, Tsinghua University, and Microsoft Research propose a theory for optimizing language model (LM) learning, emphasizing maximizing data compression ratio. They derive the Learning Law theorem, validated in experiments, showing equal contribution of examples to optimal learning. Optimized process improves LM scaling law coefficients, promising faster LM training with practical significance.
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Practical Solutions for Optimizing Language Model Learning
Introduction
With the rise of language models, there has been a focus on improving learning speed and model performance while managing computational requirements. This benefits research and industry communities.
Prior Works
Prior works have focused on designing effective architectures, utilizing rich contexts, and improving computational efficiency. Open-source alternatives and large batch optimization have been explored to overcome computational challenges.
Optimizing LM Learning
Researchers have proposed a theory for optimizing LM learning by maximizing the data compression ratio. They have derived the Learning Law theorem to elucidate optimal learning dynamics, offering promising implications for practical learning acceleration methods.
Optimal Learning of Language Models
The researchers have demonstrated principles for optimizing LM learning speed, including the optimization objective, the property of optimal learning dynamics, and the essential improvement of learning acceleration. They have proposed to minimize the area under the curve (AUC) as the optimization objective, and derived the Learning Law theorem that characterizes the property of dynamics in the LM learning process.
Results and Conclusion
Experiments on linear classification and language modeling tasks confirmed the effectiveness of the method, significantly accelerating learning and improving loss AUC. The proposed theory and method offer promising implications for faster LM training with practical significance.
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