抽象化：一种面向持续学习的内存高效归纳偏置

摘要

现实世界具有非平稳性和无限复杂性，这要求智能体能够在不承担从头训练的高昂代价下持续学习。尽管在线持续学习为此提供了框架，但新知识的学习往往会干扰已掌握知识，导致遗忘和泛化能力下降。为解决这一问题，我们提出抽象增强训练（AAT），通过损失函数层面的改进促使模型捕捉样本间潜在的关联结构。该方法通过对具体实例及其抽象表征进行联合优化，引入一种内存高效的归纳偏置，从而在严格在线数据流中实现稳定学习，无需依赖经验回放缓冲区。为捕捉抽象的多维特性，我们在两个基准测试中引入并评估AAT：一是通过实体掩码实现抽象的可控关系数据集，二是通过共享谚语表达抽象的叙事数据集。实验结果表明，尽管AAT无需额外内存且仅对训练目标进行最小改动，其性能已达到或超越强经验回放（ER）基线。这项工作揭示了结构抽象可作为ER的一种强大且无需内存的替代方案。

English

The real world is non-stationary and infinitely complex, requiring intelligent agents to learn continually without the prohibitive cost of retraining from scratch. While online continual learning offers a framework for this setting, learning new information often interferes with previously acquired knowledge, causes forgetting and degraded generalization. To address this, we propose Abstraction-Augmented Training (AAT), a loss-level modification encouraging models to capture the latent relational structure shared across examples. By jointly optimizing over concrete instances and their abstract representations, AAT introduces a memory-efficient inductive bias that stabilizes learning in strictly online data streams, eliminating the need for a replay buffer. To capture the multi-faceted nature of abstraction, we introduce and evaluate AAT on two benchmarks: a controlled relational dataset where abstraction is realized through entity masking, and a narrative dataset where abstraction is expressed through shared proverbs. Our results show that AAT achieves performance comparable to or exceeding strong experience replay (ER) baselines, despite requiring zero additional memory and only minimal changes to the training objective. This work highlights structural abstraction as a powerful, memory-free alternative to ER.

抽象化：一种面向持续学习的内存高效归纳偏置

Abstraction as a Memory-Efficient Inductive Bias for Continual Learning

摘要

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