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

Build a lightweight training harness that converts binary reward signals into dense supervision signals through self-distillation. This allows models to learn effectively in sparse reward environments without external teacher labels.

Suggested repo: self-distill

"Turn sparse RL rewards into dense token-level training signals automatically."

Estimated effort: 40h