arXiv:2603.17198v2 Announce Type: replace-cross Abstract: A foundational principle in cognitive science holds that intelligent agents do not learn by storing experiences as isolated instances, but by forming abstract schemas that capture relational structure shared across situations. Even though this claim is well supported by behavioral and neuroimaging studies, its role as a computational training signal in language models remains underexplored. We target this gap in the setting of non-stationary language model training, asking does biasing learning toward structural abstraction reduce catas
The continuous improvement of language models necessitates more efficient and robust training methodologies, especially as models scale and encounter issues like catastrophic forgetting in non-stationary environments.
This research suggests a fundamental improvement in how language models learn, potentially leading to more stable, adaptable, and computationally efficient AI systems that can better generalize knowledge.
The approach to training large language models may shift towards incorporating explicit inductive biases for structural abstraction, leading to more resilient and intelligent AI.
- · AI researchers
- · Large language model developers
- · Cognitive science
- · Inefficient AI training methods
Language models become more robust to new, out-of-distribution data without suffering significant performance degradation on previous knowledge.
This improved robustness allows for more continuous and adaptive learning in real-world deployments, reducing the need for frequent full retraining.
The development of truly 'understanding' AI systems that can reason more effectively by abstracting fundamental principles rather than just memorizing patterns.
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Read at arXiv cs.CL