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About this paper
William T. Redman, Erik C. Johnson, Brian Robinson
Researchers from Johns Hopkins Applied Physics Lab explored how Transformer models perform continual compositional reasoning on a synthetic task, identifying that recurrent ALBERT learns generalizable algorithmic solutions leading to strong forward transfer, whereas feedforward BERT adopts non-generalizable shortcut strategies. The study showed that simple replay buffers mitigate catastrophic forgetting, yet effective cross-experience composition requires explicitly combined training data, especially for ALBERT.
Key insights
- ALBERT's architecture fosters generalizable, "For loop-esque" algorithmic solutions, leading to robust generalization and strong positive forward transfer.
- BERT's architecture tends to develop non-generalizable "shortcut solutions" that impair generalization and result in weaker forward transfer.
- While replay buffers effectively prevent catastrophic forgetting, integrating knowledge across distinct experiences for true composition requires more explicit data combination strategies, like incremental training.
Summary by alphaXiv.
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