The Same Bug, Four Times, Three of Them Mine
Originally published at ai.bedvibe.studio . Most validation tooling has two states: it passed, or it failed. Everything that was not actually evaluated has to be forced into one of them — and it is wrong in both directions. I did not work that out from first principles. I worked it out by shipping the same defect four times. One: a run that learned nothing, reported as healthy In trainproof , a…
The issue of validation tooling failing to accurately assess certain conditions was discovered multiple times, ultimately leading to the creation of a library to address the problem. The first instance occurred when a run was executed that learned nothing, but was reported as healthy due to the tool's inability to properly handle division by zero.
The second instance involved a similar scenario, where a training run with a loss of exactly 0.0 on every step was also reported as healthy. The third instance was discovered in infrastructure compliance, where a framework document described things that could not be verified by a machine, leading to a situation where unevaluated items appeared identical to those that had passed.
The fourth instance was found while the reporter was pleased with themselves, where three instances were detected while evaluating a retrieval experiment. This led to the conclusion that the current validation tooling states were insufficient, and a new taxonomy was needed to accurately reflect the different conditions.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
