无需梯度信息也能防止遗忘，零梯度优化新方法突破持续学习瓶颈
ZeroFlow: Overcoming Catastrophic Forgetting is Easier than You Think
- 仅用前向传播实现参数更新，突破传统依赖梯度的限制
- 在多个数据集上验证前向方法可有效缓解遗忘，性能接近带梯度方法
- 为黑箱系统、硬件受限场景提供实用持续学习解决方案
反向传播为克服灾难性遗忘提供了通用框架。尽管SGD和Adam等优化器常用于持续学习与持续预训练中的权重更新，但实际中因黑箱API、硬件限制或不可微系统，梯度信息获取并不总是可行，这一挑战称为梯度禁令。为此，我们提出ZeroFlow——首个评估无梯度优化算法应对遗忘的基准。ZeroFlow在多种算法、遗忘场景和数据集上测试了一系列基于前向传播的方法。结果表明，仅靠前向传播即可有效缓解遗忘。我们揭示了新的优化原则，凸显前向传播方法在缓解遗忘、处理任务冲突及降低内存需求方面的潜力，并提出新改进方案进一步增强其抗遗忘能力。本工作为推进前向传播驱动的持续学习方法提供了关键工具与洞见。
原文摘要 · Abstract (English)
Backpropagation provides a generalized configuration for overcoming catastrophic forgetting. Optimizers such as SGD and Adam are commonly used for weight updates in continual learning and continual pre-training. However, access to gradient information is not always feasible in practice due to black-box APIs, hardware constraints, or non-differentiable systems, a challenge we refer to as the gradient bans. To bridge this gap, we introduce ZeroFlow, the first benchmark designed to evaluate gradient-free optimization algorithms for overcoming forgetting. ZeroFlow examines a suite of forward pass-based methods across various algorithms, forgetting scenarios, and datasets. Our results show that forward passes alone can be sufficient to mitigate forgetting. We uncover novel optimization principles that highlight the potential of forward pass-based methods in mitigating forgetting, managing task conflicts, and reducing memory demands. Additionally, we propose new enhancements that further improve forgetting resistance using only forward passes. This work provides essential tools and insights to advance the development of forward-pass-based methods for continual learning.
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