Code Review Dataset drops 355k review rows

This is the kind of dataset AI coding research has been missing: real reviewer feedback tied to actual code edits instead of synthetic instruction fluff.

• –The strongest signal here is the triplet structure: diff context, reviewer comment, and resulting code change all in one row
• –Negative examples matter almost as much as positive ones because they teach models when clean code should pass without noisy comments
• –Coverage across 725 repos and 37 languages makes it more useful for generalist coding models than single-language benchmark sets
• –The permissive-license filtering lowers legal friction for teams experimenting with fine-tuning on real OSS review data
• –The reported model gains are promising, but they are still self-reported metrics rather than an independently validated benchmark