Grokking Metrics Overstate Compression, Lag Generalization
Summary
This study reveals that common metrics for "grokking" in neural networks, such as effective rank, significantly overstate the true compression achieved at the grokking transition and lag behind the network's generalization by thousands of steps. It introduces an audit framework to accurately measure representation compression.
Why it matters
Accurate measurement of grokking and representation compression is crucial for understanding how neural networks learn and generalize, impacting model design and training strategies.
How to implement this in your domain
- 1Apply the proposed audit framework to evaluate grokking and representation compression in your own neural network experiments.
- 2Re-evaluate existing research findings on grokking, considering the potential for overstated compression metrics.
- 3Investigate the impact of architectural choices like LayerNorm on the timing and extent of representation compression.
- 4Develop training strategies that explicitly account for the lag between generalization and full representation compression.
Who benefits
Key takeaways
- Grokking metrics like effective rank often overstate true representation compression.
- Representation compression significantly lags behind a network's generalization phase.
- Architectural components like LayerNorm can influence the timing of compression.
- A new audit framework is proposed for more accurate measurement of grokking phenomena.
Original post by Truong Xuan Khanh
"arXiv:2607.06639v1 Announce Type: new Abstract: On modular arithmetic, a network's embedding keeps compressing for tens of thousands of steps after it has already generalized. Reading effective rank at the grokking transition overstates the converged value by 3-5x on an MLP, and…"
View on XOriginally posted by Truong Xuan Khanh on X · view source
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