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  "text": "Self-Distillation Zero: Self-Revision Turns Binary Rewards into Dense Supervision\n\n\"SD-ZERO trains a single model to play two roles: a Generator, which produces an initial response, and a Reviser, which conditions on that response and its binary reward to produce an improved response. We then perform on-policy self-distillation to distill the reviser into the generator, using the reviser’s token distributions conditioned on the generator’s response and its reward as supervision. In effect, SD-ZERO trains the model to transform binary rewards into dense token-level self-supervision.\"",
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