Self-distillation's reasoning gains don't survive new problems: coding help stays inside the training set, and gradients often point away from policy gradients
Two self-distillation methods lose or reverse their reported gains on unseen problems across code, science, tool use and math, and the math improvement comes mainly from transferring thinking-mode behavior rather than from privileged information such as reference solutions.