The Role of Feedback Alignment in Self-Distillation
Why teaching AI to learn from feedback works better when advice matches how it thinks
Language models learn to improve their reasoning when feedback is aligned with their actual step-by-step thought process, rather than just shown a correct answer. Step-by-step critiques outperformed traditional reward signals by 16 points and reference solutions by 5 points, because they fix only the broken parts of reasoning while leaving correct steps alone.
As AI systems tackle harder problems, teaching them to retain improvements without always having feedback present matters for real-world deployment. The finding that structural alignment between feedback and reasoning is crucial suggests companies and researchers can make AI training far more efficient—fixing only what's actually wrong rather than asking models to rethink entire solutions that were mostly correct.