The problem is there is no good metric to describe code quality, it cannot be RLd and that's likely why latest models have such problems with slop. It is great to raise the conversation so that labs focus on this more.

Picking specific metrics will probably not work, it would be a mix of Goodhart's law with Bitter lesson. Maybe picking and labeling quality repos, having whole suite of metrics as input features and training some traditional AI classifiers to steer the LLM training.