arXiv:2604.13082v2 Announce Type: replace-cross Abstract: Grokking in transformers trained on algorithmic tasks is characterized by a long delay between training-set fit and abrupt generalization, but the source of that delay remains poorly understood. In encoder-decoder arithmetic models, we argue that this delay reflects limited access to already learned structure rather than failure to acquire that structure in the first place. We study one-step Collatz prediction and find that the encoder organizes parity and residue structure within the first few thousand training steps, while output accu
This research provides a deeper understanding of 'grokking' and generalization mechanisms in AI, which is a current frontier in AI development.
Understanding how AI models generalize is crucial for building more robust, reliable, and truly intelligent systems capable of complex reasoning beyond interpolation.
This research shifts the understanding of generalization delays from a failure to acquire knowledge to an issue of access to already learned structures within models.
- · AI researchers
- · Deep learning framework developers
- · Companies building advanced AI systems
- · AI models without robust generalization
- · Purely statistical learning approaches
Improved architectures and training methodologies that facilitate earlier access to learned representations.
Faster development of AI models capable of complex arithmetic and logical reasoning.
Acceleration of research into true artificial general intelligence by refining models' cognitive processes.
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Read at arXiv cs.AI