Explain how scaling laws should influence safety evaluation of large models.

Chinchilla-style scaling argues that compute-optimal training balances parameter count and token count instead of making the model very large and undertrained. The useful implication is to allocate compute jointly across model size, data, and training duration.

Use equations or tensor shapes where they clarify the claim, then name an experiment or ablation that would distinguish competing explanations.