Simple Strategies to Continually Pre-train Large Language Models with Less Compute

This paper tackles the challenge of efficiently updating large language models (LLMs) with new data. LLMs are typically trained on massive datasets, and retraining them from scratch every time new information becomes available is computationally expensive and inefficient. The paper proposes a simple and scalable approach for continual pre-training, enabling LLMs to stay current with minimal computational resources. The key idea is to use a combination of learning rate adjustments and data replay. The researchers introduce learning rate re-warming, gradually increasing the learning rate during training to adapt to new data, followed by re-decaying, gradually decreasing the learning rate to stabilize the model. Additionally, the model is periodically exposed to data it was trained on previously to prevent forgetting. Experiments were conducted on both smaller and larger LLMs, demonstrating the effectiveness of this approach across different distribution shifts, including English-to-English and English-to-German. The continual pre-training methods achieved performance comparable to full retraining while significantly reducing computational requirements. The research highlights the practical implications of this solution for deploying LLMs in real-world scenarios, allowing them to be constantly updated with new information efficiently. While the paper focuses on pre-training, future research could explore applying these continual learning techniques to fine-tuning LLMs for specific downstream tasks. The authors acknowledge that there might be even more sophisticated continual learning techniques that could further improve performance. This research represents a significant step towards more efficient and practical large language model pre-training, paving the way for more powerful and adaptable AI systems.