arXiv:2605.20441v1 Announce Type: new Abstract: Transformers trained on modular arithmetic exhibit sharp transitions between memorization, generalization, and collapse. We show that weight decay acts as a scalar empirical control parameter for these regimes, and introduce two cheap online diagnostics, mean pairwise attention-head cosine similarity and entropy standard deviation, that track training dynamics from attention activations alone and complement loss-landscape diagnostics at lower compute cost. Across eleven experimental conditions and three model scales (0.82M to 85M parameters), the
This research provides new, computationally efficient methods for diagnosing the training dynamics of foundational AI models, specifically transformers, at a time when model size and complexity are rapidly increasing.
Improved diagnostics for large language models (LLMs) can lead to more efficient and stable training, which directly impacts the cost and performance of advanced AI systems, influencing their development and deployment across various sectors.
The introduction of 'cheap online diagnostics' for AI model training offers developers and researchers new tools to understand and optimize model behavior without requiring extensive computational resources.
- · AI researchers
- · Large language model developers
- · Cloud computing providers
- · AI-driven software companies
- · Teams without advanced diagnostic tools
- · Inefficient AI training methodologies
More robust and efficient training of large AI models becomes possible due to better diagnostic insights.
Accelerated development cycles for new AI capabilities and applications emerge as model optimization improves.
Enhanced AI performance contributes to the broader adoption and integration of AI across industries, potentially impacting labor markets and national competitiveness.
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