TLDR: GeRe is a new framework for continual learning in large language models (LLMs) that tackles catastrophic forgetting. It uses a small, fixed set of general pretraining texts as “replay samples” to help LLMs retain their broad knowledge and improve performance on new tasks. An innovative “Threshold-based Margin (TM) loss” further enhances this by maintaining consistent neural activation states, proving more robust than traditional methods. This approach simplifies continual learning by eliminating the need for task-specific replay data.
Large Language Models (LLMs) are at the forefront of artificial intelligence, but their ability to continuously learn and adapt to new information without forgetting old knowledge remains a significant challenge. This issue, known as catastrophic forgetting, manifests in two main ways: LLMs tend to lose their general capabilities (like world knowledge or basic instruction-following) and their performance on previously learned tasks sharply declines when fine-tuned on new data.
To address this persistent problem, researchers Yunan Zhang, Shuoran Jiang, Mengchen Zhao, Yuefeng Li, Yang Fan, Xiangping Wu, and Qingcai Chen have introduced a novel framework called General Sample Replay (GeRe). This approach offers a simple yet stable method to combat forgetting in LLMs.
Traditionally, continual learning solutions for LLMs often involve replaying samples from past tasks. However, these methods can be cumbersome, requiring the laborious collection and management of increasing sets of task-specific replay data. GeRe simplifies this by proposing a fixed, small set of general pretraining texts that can be reused throughout the entire continual learning process. The core idea is that by revisiting these general samples, the LLM can retain its foundational knowledge, which in turn helps it maintain performance across a sequence of new tasks.
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How GeRe Works
Beyond simply replaying general samples, GeRe introduces an enhanced optimization method called Threshold-based Margin (TM) loss. This innovative loss function focuses on maintaining the consistency of the LLM’s internal neural activation states during replay learning. Inspired by how critical information is sparsely distributed across activated neurons in the human brain, TM loss categorizes neuron activations into positive, negative, and non-activated states. It then uses a threshold-based margin to guide the optimization, ensuring that the model’s internal representations remain stable even as it learns new information.
The researchers conducted controlled experiments using the Llama-3.1-8B model across 15 diverse downstream tasks. Their findings were significant: a small, fixed set of pre-collected general replay samples proved sufficient to both preserve the LLM’s general capabilities and improve its overall performance on sequential tasks. This challenges the conventional need for task-specific replay samples, paving the way for more efficient and practical continual learning.
The study also rigorously compared TM loss with other common replay strategies, including vanilla label fitting, logit imitation via KL divergence, and feature imitation via L1/L2 losses. Results consistently demonstrated that TM loss not only improved performance but also exhibited better robustness. This robustness was further validated through analyses of learning rate variations and visualizations of the optimization landscape, showing that GeRe maintains stable performance even under conditions that typically lead to forgetting in other methods.
In essence, GeRe offers a powerful and practical solution for continual learning in LLMs. By leveraging a fixed set of general replay samples and an intelligent activation state-constrained optimization, it effectively mitigates catastrophic forgetting, allowing LLMs to continuously evolve and adapt without sacrificing their accumulated knowledge. For more technical details, you can refer to the full research paper here.
