Efficient Continual Pre-training by Mitigating the Stability Gap
Authors: name: Yiduo Guo, name: Jie Fu, name: Huishuai Zhang, name: Dongyan Zhao, name: Yikang Shen

Continual pre-training has increasingly become the predominant approach for
adapting Large Language Models (LLMs) to new domains. This process involves
updating the pre-trained LLM with a corpus from a new domain, resulting in a
shift in the training distribution. To study the behavior of LLMs during this
shift, we measured the model's performance throughout the continual
pre-training process. we observed a temporary performance drop at the
beginning, followed by a recovery phase, a phenomenon known as the "stability
gap," previously noted in vision models classifying new classes. To address
this issue and enhance LLM performance within a fixed compute budget, we
propose three effective strategies: (1) Continually pre-training the LLM on a
subset with a proper size for multiple epochs, resulting in faster performance
recovery than pre-training the LLM on a large corpus in a single epoch; (2)
Pre-training the LLM only on high-quality sub-corpus, which rapidly boosts
domain performance; and (3) Using a data mixture similar to the pre-training
data to reduce distribution gap. We conduct various experiments on Llama-family
models to validate the effectiveness of our strategies in both medical
continual pre-training and instruction tuning. For example, our strategies
improve the average medical task performance of the OpenLlama-3B model from
36.2% to 40.7% with only 40% of the original training budget and enhance the
average general task performance without causing forgetting. Furthermore, we
apply our strategies to the Llama-3-8B model. The resulting model,
Llama-3-Physician, achieves the best medical performance among current
open-source models, and performs comparably to or even better than GPT-4 on
several medical benchmarks. We release our models at
https://huggingface.co/YiDuo1999/Llama-3-Physician-8B-Instruct.