基于动态梯度引导的多模态持续指令微调
Multimodal Continual Instruction Tuning with Dynamic Gradient Guidance
把灾难性遗忘重新表述为「旧任务梯度缺失」，并用当前参数与历史最优参数之间的方向向量来近似该梯度，这一思路可直接迁移到视觉语言模型的持续指令微调与多任务适配中，配合伯努利采样在稳定性与可塑性之间动态权衡。
原文依据 conceptualizing it as a problem of missing gradients from old tasks during new task learning
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多模态持续指令微调使多模态大语言模型能够在已有知识的基础上顺序地适应新任务。然而，这种持续学习范式面临灾难性遗忘这一重大挑战，即学习新任务会导致在先前任务上的性能下降。本文通过将灾难性遗忘理解为新任务学习过程中旧任务梯度缺失的问题，为理解该现象提供了新的视角。我们的方法利用参数空间的几何性质来近似这些缺失的梯度，具体而言，使用当前参数与先前最优参数之间的方向向量作为梯度引导。该近似梯度可进一步与来自有限回放缓冲区的真实梯度相结合，并通过伯努利采样策略进行调节，从而动态平衡模型的稳定性与可塑性。在多模态持续指令微调数据集上的大量实验表明，我们的方法在不进行模型扩展的情况下达到了最优性能，在保持紧凑架构的同时有效缓解了灾难性遗忘。
英文原文摘要 1213 字符
Multimodal continual instruction tuning enables multimodal large language models to sequentially adapt to new tasks while building upon previously acquired knowledge. However, this continual learning paradigm faces the significant challenge of catastrophic forgetting, where learning new tasks leads to performance degradation on previous ones. In this paper, we introduce a novel insight into catastrophic forgetting by conceptualizing it as a problem of missing gradients from old tasks during new task learning. Our approach approximates these missing gradients by leveraging the geometric properties of the parameter space, specifically using the directional vector between current parameters and previously optimal parameters as gradient guidance. This approximated gradient can be further integrated with real gradients from a limited replay buffer and regulated by a Bernoulli sampling strategy that dynamically balances model stability and plasticity. Extensive experiments on multimodal continual instruction tuning datasets demonstrate that our method achieves state-of-the-art performance without model expansion, effectively mitigating catastrophic forgetting while maintaining a compact architecture.