VISTA方法改进了OPSD蒸馏技术，通过验证器指导教师自适应，在数学推理任务上显著提升性能。
VISTA方法在AIME24、AIME25和HMMT25基准测试中，使用Qwen3模型在1.7B、4B和8B规模下均取得最高Avg@12成绩。该方法通过结果验证的rollout来调整教师模型分布，仅针对top-k位置进行KL散度优化。VISTA在保持标准OPSD学生更新的同时，实现了对OPSD方法0.6、0.7和2.1个百分点的性能提升。
VISTA: Verifier-Informed Student-to-Teacher Adaptation for On-Policy Self-Distillation
On-policy self-distillation (OPSD) improves reasoning by training a problem-only student on its own rollouts using dense token-level supervision from a privileged teacher that also sees a reference solution. However, standard OPSD treats the teacher distribution as a fixed target along the student's rollout and updates only the student %, although -- even though privileged conditioning does not guarantee that the teacher always provides the most appropriate target for problem-only reasoning. This one-way supervision can therefore misdirect the student when the teacher distribution is misaligned with valid student reasoning. We therefore introduce Verifier-Informed Student-to-Teacher Adaptation (VISTA), which preserves the standard OPSD student update while using outcome-verified rollouts to adapt the teacher toward the student distribution. Within each verified rollout, VISTA further restricts this adaptation to the top-$k$ positions with the largest teacher--student KL divergence. Notably, VISTA reuses the rollout and loss function from standard OPSD, introducing no additional sampling or separate reward objective. Across AIME24, AIME25, and HMMT25 with Qwen3 models at 1.7B, 4B, and 8B, VISTA achieves the highest Avg@12 at every scale, improving over OPSD by $0.6$, $0.7$, and $2.1$ points, respectively. These results demonstrate the value of student supervision from outcome-verified rollouts and highlight student-to-teacher adaptation as a promising direction for OPSD.