Generalization Scaling Laws Under Extreme Compression
andrewgwils · x · 2026-07-15
The authors state that by pushing compression to its limits, they demonstrated that the generalization gap decreases as a power law with scale.
This result is considered crucial for the validity of the modern scaling paradigm: if the generalization gap did not continue to shrink with scale, models would hit a bottleneck where training loss keeps dropping, but test performance stalls.
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