Filtering training data for code-fixing tasks at both trajectory and step levels beats training on all successful examples—quality and relevance of supervision matter more than quantity.
SWE-Prime improves how AI models learn to fix software bugs by being smarter about which training examples to use. Instead of training on all successful bug fixes, it filters trajectories (problem-solving paths) at two levels—first selecting high-quality complete solutions, then identifying which individual steps within those solutions are actually worth learning from.