Large language models (LLMs) are cornerstone technologies in AI, driving advancements across various fields. However, the traditional approach of re-training LLMs with every new data set is both costly and computationally inefficient. This paper presents a novel approach, focusing on continual pre-training, which allows for the incremental updating of LLMs without the need for full re-training, significantly saving computational resources.
The key findings include:
Learning Rate Adjustments: The study demonstrates the necessity of re-warming and re-decaying the learning rate to adapt to new datasets effectively. This approach significantly outperforms the traditional method of continuing training from the last small learning rate.
The Role of Replay: Integrating a replay mechanism, where a small percentage of the old dataset is mixed with the new data, substantially mitigates the forgetting of previously learned information. This is especially crucial for stronger distribution shifts, such as transitioning from one language to another.
Scalability and Efficiency: The proposed strategies are scalable and efficient, showing promising results in both weak and strong distribution shifts and across different model sizes, including up to 10 billion parameters.
Continual Pre-training vs. Combined Dataset Training: Unlike common practices such as the one discussed in the BloombergGPT paper, this research shows that continued pretraining can achieve similar or better validation loss and downstream task performance without the need to train on the combined dataset of D1 and D2.
Practical Application of Continued Pre-training: The researchers outline a practical approach to continued pre-training involving re-warming and re-decaying the learning rate and mixing a small portion of the old data with the new. This method prevents catastrophic forgetting and maintains model performance on previous tasks.
Infinite Learning Rate Schedule: The paper also explores the use of an "infinite learning rate schedule" in pre-training, finding that the typical re-warming and re-decaying strategy performs just as well, simplifying the pre-training process.
This innovative approach deviates from traditional learning rate schedules by not having a predetermined endpoint for the learning rate decay, allowing for a more flexible and potentially unending adaptation phase during pre-training. This method is particularly useful in scenarios where training might continue indefinitely, or for very long periods, as it keeps the learning rate dynamic and adaptable over time.
The infinite learning rate schedule is designed to accommodate the continuous influx of new data, making it an intriguing option for the continual pre-training of large language models. It aims to maintain a balance where the model can learn from new data without forgetting previously acquired knowledge, a challenge commonly known as catastrophic forgetting.
However, the paper's findings reveal an interesting conclusion: the more traditional strategy of re-warming and re-decaying the learning rate, where the learning rate is first increased (re-warmed) to reinvigorate the model's training on new data and then gradually decreased (re-decayed) to stabilize learning, performs comparably to the infinite learning rate schedule. This similarity in performance suggests that while the infinite learning rate schedule presents a novel approach to managing the learning rate over extended training periods, the traditional method of re-warming and re-decaying remains a viable and effective strategy for continually pre-training large language models.
This conclusion simplifies the pre-training process by indicating that adopting an infinite learning rate schedule may not provide significant advantages over the established method of re-warming and re-decaying the learning rate. Therefore, practitioners can continue to use the more straightforward approach without compromising on the effectiveness of continual pre-training, thereby streamlining the model updating process while ensuring high levels of adaptability and retention of previously learned information.
The study concludes that simple yet scalable continual learning strategies can effectively update LLMs with minimal computational costs, potentially revolutionizing how these models are maintained and improved over time.
A critical aspect of the continual pre-training process is the effective adjustment of the learning rate, which involves re-warming and re-decaying. Re-warming refers to the process of increasing the learning rate from a lower value up to a higher value, often to the maximum learning rate used during the initial training phases. This step is crucial as it "reheats" the model's capacity to learn from the new data after having settled into a state of minimal learning progress due to the previously low learning rate.
Following the re-warming phase, re-decaying the learning rate involves gradually decreasing it again, typically following a pre-defined schedule such as a cosine decay. This decrease helps the model to fine-tune its weights on the new data, preventing overfitting and ensuring that the model can generalize well from the newly learned knowledge.
These steps are fundamental in adapting the model to new data sets without forgetting previously learned information. They simulate a training lifecycle that allows the model to learn from new data effectively while retaining what it has already learned, thus overcoming the challenges of catastrophic forgetting.
Created 2024-03-15T16:26:47-07:00, updated 2024-03-16T07:35:17-07:00 · History · Edit