Two Speeds of Learning: A Representation-Readout Decomposition of Grokking and Double Descent
arXiv:2605.27078v1 Announce Type: new Abstract: Training loss and accuracy are the standard signals used to monitor generalization during deep neural network training. Two well-documented phenomena complicate this picture: in grokking, train loss falls rapidly while test performance improves abruptly only after a long delay; in epoch-wise double descent, train loss decreases monotonically while test loss or error rises and falls. Existing accounts are often task-specific, and a task-agnostic analysis framework for diagnosing and explaining these phenomena across realistic tasks and architectur
This research provides a deeper, framework-level understanding of fundamental deep learning phenomena (grokking, double descent) that are highly relevant to the current rapid advancements in AI models.
A more robust, task-agnostic understanding of model generalization is critical for designing more efficient, reliable, and predictable AI systems, impacting development cycles and deployment strategies.
The proposed 'representation-readout' decomposition offers a new analytical lens, potentially allowing faster debugging and optimization of deep learning models by dissecting training dynamics.
- · AI researchers
- · Deep learning practitioners
- · Model developers
- · Ad-hoc AI development methods
- · Researchers relying on purely empirical trial-and-error without theoretical unde
Improved understanding and diagnosability of deep learning model training behaviors like grokking and double descent.
More predictable and efficient development of large-scale AI models, as generalization patterns become clearer.
Acceleration in AI deployment across various sectors due to enhanced model reliability and interpretability regarding generalization.
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Read at arXiv cs.LG