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.
More from Research
- Diamandis: AI Cancer Navigator and $100 Genome Sequencing Close the Care Gap — PeterDiamandis · 2026-10-27
- EMNLP Oral: training-free Nexus Sampling cuts KV-cache memory 10x; OpenResearcher tops BrowseComp-Plus at 54.8% — yuz9yuz · 2026-10-27
- ICML 2026 position paper: LLM 'thinking traces' are a harmful anthropomorphism — rao2z · 2026-10-27
- Forking gap follows a power law in context frequency; masking rare n-grams shrinks it — ZimingLiu11 · 2026-10-27
- How forking happens: n-gram tables split logits into non-interacting subspaces — ZimingLiu11 · 2026-10-27
- The best LoRA explainer video yet: rank constraints meet intrinsic dimension — sophiamyang · 2026-10-27