New AI training method SD-Zero converts sparse binary rewards into rich learning signals without needing external teachers or high-quality data.

Self-Distillation Zero (SD-Zero) addresses the limitation of reinforcement learning by converting binary rewards into dense token-level supervision

Uses a single model with dual roles: Generator produces initial responses, Reviser improves them using binary reward feedback

Requires no external teacher model or costly high-quality demonstrations, making it more practical and cost-effective

Performs on-policy self-distillation to transfer knowledge from Reviser back to Generator using token distributions

Demonstrates substantially better training sample efficiency compared to traditional reinforcement learning approaches