Theory separates locality from weight sharing in convolutional learning
With uncorrelated inputs a convolutional student learns exactly P times faster, but spatial correlation can erase that edge.
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With uncorrelated inputs a convolutional student learns exactly P times faster, but spatial correlation can erase that edge.
R2S beat three agentic on-policy methods in all 12 teacher-student-environment cells, by 12.8 success points on average.
CriticOPD uses teacher critiques to restore recoverability in on-policy distillation, lifting two Qwen3 students on math benchmarks.
SCOUT adapts teachers to student-generated prefixes and improves on-policy distillation across scales and domains.
DuoOPD uses joint teacher-student outcomes to improve multi-task on-policy distillation over standard OPD.
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