Researchers propose rubric-conditioned self-distillation, a new method for post-training reasoning language models that reduces reliance on expensive and potentially noisy chain-of-thought annotations. This approach uses evaluative feedback to improve model performance without requiring detailed rationales. The method aims to enhance model accuracy and efficiency by leveraging verified rewards.