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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 10·5 min read
Post-Grokking Collapse at the Representation-Readout Interface in Muon-Trained Transformers
TL;DR
Muon-trained transformers exhibit a post-grokking collapse at the representation-readout interface, leading to significant generalization failures despite initial success in learning modular addition tasks.
✦ Why It Matters
Consider freezing model components during training to prevent performance collapse in similar transformer architectures.
Key Takeaways
- Explore freezing model components to enhance stability during training.
- Investigate the impact of representation-readout interfaces on model performance.
- Utilize Fourier filtering to analyze model behavior under different conditions.
- Adjust training strategies to maintain generalization in transformer models.
How It Works
Muon-trained transformers initially learn tasks effectively but face a collapse in generalization due to interactions at the representation-readout interface. By freezing certain model components, researchers can prevent this collapse, indicating that specific architectural choices significantly influence learning stability.