eval-before-optimize

To verify post-training eval results are statistically significant, repeat evaluations across seeds to establish baseline variance and compute required sample size to confirm results exceed the noise floor.

Measure eval noise floor for RL experiments by repeating evaluations across multiple seeds and runs, establishing a robust baseline variance to distinguish actual learning from noise.

Evaluation variance fluctuates between post-training runs due to seed differences and sampling randomness, requiring repeated evals to establish a baseline before claiming improvements.

The best way to prevent mistaking noise for signal in ES optimization is applying threshold-based guardrails after repeated evals to validate statistical significance before proceeding.

Know when to continue optimization after post-training by running a baseline variance check and applying threshold-based decisions to confirm observed improvements are confident enough to pursue.

Repeated evals work for small-scale post-training experiments across varying models and evaluation setups, computing required sample size to determine when results become statistically meaningful.