How would you evaluate whether your post-training run actually improved the model?

Evaluation must separate the proxy reward used during training from a held-out evaluation harness that is harder to game. The harness should include both capability benchmarks and a regression floor to ensure performance hasn’t degraded in critical areas. You must also actively watch for length confounds and the overoptimization cliff, where the model appears to improve on the reward but degrades in actual capability.