Failed reasoning traces aren't just noise—they contain distributional signatures that predict whether a problem needs more compute (sampling) or a different strategy (intervention), letting you route resources more efficiently without retraining.
When language models fail at reasoning tasks, their failed attempts contain hidden patterns that reveal whether the failure is fixable through retrying or requires a different intervention.