Token-level correction during annotation is significantly faster than full rewriting and produces on-policy training data that preserves the model's natural generation patterns while providing precise supervision signals.
onPanda is an interactive annotation tool that helps create training data for AI models by letting annotators correct responses token-by-token. Instead of rewriting entire outputs, annotators find the first mistake, fix it, and let the model regenerate from that point.