Self-Distillation Zero: Self-Revision Turns Binary Rewards into Dense Supervision

Self-Distillation Zero is a training technique that uses self-revision to convert binary reward signals into dense supervision for model learning. The method addresses the limitation of sparse feedback by generating richer training signals, improving model optimization in reinforcement learning scenarios.