Amazon research explains why ML research agents don't overfit benchmarks
Original titleYears of iterating against the same benchmarks should, by textbook logic, produce overfitting. It largely doesn't.
AISummary
Amazon Science researchers propose that machine learning research agents avoid overfitting benchmarks despite years of iteration against the same tests. They attribute this to generalizable strategies being expressed compactly, leaving no room for memorization, while overfitting strategies fail to survive a compression bottleneck.
Source: Amazon Science · x.comPublished · added here