Scaling laws are a planning tool, not a guarantee of capability scaling
IgorCarron · x · 2026-07-24
- A quote from Lilian Weng’s essay argues that scaling laws are useful precisely because they are easy to misuse.
- The post highlights that the essay walks through how training loss scales with model size, data, and compute, and why Kaplan-style and Chinchilla-style recipes can diverge.
- Practical takeaways: fit power laws on cheap small runs, count parameters and tokens consistently, separate the infinite-data story from data-limited training, and treat fitting choices as part of the result.
- The core warning is that smooth log-log curves can still lead to very different trillion-token training decisions; scaling laws are a planning tool, not a guarantee of downstream capability scaling.
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