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

Self-Distillation Zero (SD-Zero) is a novel post-training method designed to be more training sample-efficient than traditional reinforcement learning, without requiring external teachers or high-quality demonstrations. It operates by having a single model act as both a Generator and a Reviser, using the Reviser's improved responses and token distributions to provide dense supervision for the Generator through on-policy self-distillation.