In the recent paper Simple and Scalable Strategies to Continually Pre-train Large Language Models, researchers propose a streamlined approach to updating LLMs with new data by re-warming and re-decaying learning rates and replaying previous datasets. This method achieves similar performance to models trained from scratch, with significantly lower computational costs, even when facing shifts between different language pre-training datasets.

• Demonstrates the efficacy of continual learning strategies for LLMs.
• Approached tested on both weak (English-English) and significant (English-German) distribution shifts.
• Method matches fully re-trained LLMs performance while using less compute.
• Proposes alternatives to the cosine learning rate schedule to combat forgetting.

By adopting continual learning strategies, this research underscores the potential for LLMs to be updated more efficiently, opening doors to rapidly incorporating emerging data trends and linguistic patterns while maintaining high performance metrics.