Simplifying Continual Pre-training of Large Language Models
Sameer Maurya
Manager / Senior Analyst at Bank of America | Data scientist | GenAI | LLM | Risk Management
Introduction
Large language models (LLMs) have revolutionized natural language processing, driving advancements in fields like text generation, visual understanding, and more. However, continually re-training these models from scratch is prohibitively expensive. This blog explores scalable strategies to continually pre-train LLMs, maintaining performance while significantly reducing computational costs.
Abstract and Key Findings
The study investigates efficient continual pre-training methods, focusing on learning rate (LR) re-warming, LR re-decaying, and replaying previous data. These strategies enable LLMs to match the performance of fully re-trained models, even under distribution shifts. The results show that these methods significantly reduce compute requirements while maintaining high performance across several benchmarks.
Challenges in Continual Pre-training
Traditional pre-training methods involve starting from scratch each time new data is available, leading to high computational costs. Continual pre-training offers a solution by updating existing models with new data. However, challenges such as distribution shifts, poor adaptation, and catastrophic forgetting must be addressed to ensure performance does not degrade over time.
Methodology
The study explores several key strategies:
Learning Rate Re-warming and Re-decaying: Re-increasing and subsequently re-decreasing the learning rate improves model adaptation to new data.
Replay of Previous Data: Replaying a small percentage of previous data mitigates forgetting without significantly increasing computational costs.
Infinite Learning Rate Schedules: Proposed as an alternative to fixed schedules, these schedules help prevent optimization difficulties associated with re-warming and allow smooth transitions across datasets.
Experimental Setup
The experiments were conducted using GPT-NeoX models, trained on datasets like SlimPajama, German Common Crawl, and Pile. The study tested models with 405M and 10B parameters, evaluating them under weak (English to English) and strong (English to German) distribution shifts.
Results and Analysis
The study's findings are summarized as follows:
Effectiveness of Learning Rate Strategies: Re-warming and re-decaying the learning rate significantly improved adaptation to new data across both weak and strong distribution shifts.
Impact of Replay: Even minimal replay (as low as 1%) substantially reduced forgetting, with higher percentages (up to 50%) further mitigating performance drops under strong distribution shifts.
Model Performance: Continually pre-trained models with these strategies performed comparably to models re-trained from scratch, demonstrating similar final validation losses and evaluation performance.
Discussion
The study highlights the practical implications of these strategies for large-scale LLM deployment. By efficiently updating models without full re-training, organizations can maintain state-of-the-art performance with significantly reduced computational resources. This approach is particularly valuable for applications where frequent updates are necessary to incorporate new data.
Conclusion
Continual pre-training of LLMs using simple and scalable strategies like learning rate re-warming, re-decaying, and data replay is both feasible and effective. These methods maintain high performance and reduce computational costs, making them practical for real-world applications. This study provides a roadmap for implementing continual pre-training, paving the way for more efficient and sustainable AI model development.
References