Abstraction as a Memory-Efficient Inductive Bias for Continual Learning
arXiv:2603.17198v1 Announce Type: new Abstract: 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 maskin
arXiv:2603.17198v1 Announce Type: new Abstract: 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.
Executive Summary
This article proposes Abstraction-Augmented Training (AAT), a loss-level modification that enables intelligent agents to learn continually without the prohibitive cost of retraining from scratch. AAT introduces a memory-efficient inductive bias by encouraging models to capture the latent relational structure shared across examples. The authors evaluate AAT on two benchmarks: a controlled relational dataset and a narrative dataset, demonstrating its performance comparable to or exceeding strong experience replay (ER) baselines. This work highlights structural abstraction as a powerful, memory-free alternative to ER, with significant implications for continual learning in non-stationary environments.
Key Points
- ▸ Abstraction-Augmented Training (AAT) is a loss-level modification that enables continual learning without retraining from scratch.
- ▸ AAT introduces a memory-efficient inductive bias by capturing latent relational structure across examples.
- ▸ AAT outperforms or matches strong experience replay (ER) baselines on two benchmarks with minimal changes to the training objective.
Merits
Strength in Memory Efficiency
AAT eliminates the need for a replay buffer, making it a memory-free alternative to ER, a significant advantage in resource-constrained environments.
Flexibility in Adaptation
AAT can be applied to various datasets and learning tasks, offering a flexible solution for continual learning in different domains.
Improved Performance
AAT achieves performance comparable to or exceeding ER baselines, demonstrating its effectiveness in stabilizing learning in non-stationary environments.
Demerits
Scalability Concerns
As the size of the dataset increases, the computational complexity of AAT may become a concern, potentially limiting its applicability in large-scale learning tasks.
Interpretability Challenges
AAT's reliance on abstraction may raise interpretability challenges, making it difficult to understand the underlying decision-making processes of the model.
Expert Commentary
The proposed Abstraction-Augmented Training (AAT) is a significant contribution to the field of continual learning, offering a memory-efficient and flexible solution for adapting to non-stationary environments. While AAT demonstrates impressive performance, its scalability and interpretability concerns must be addressed to ensure its widespread adoption. Furthermore, the implications of AAT's memory-free design and improved performance warrant further exploration in the context of real-world applications and policy considerations.
Recommendations
- ✓ Future research should investigate the scalability of AAT in large-scale learning tasks and explore techniques to enhance its interpretability.
- ✓ Developers and policymakers should carefully consider the implications of AAT's memory-free design and improved performance in real-world applications and policy frameworks.
