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This paper proposes a new method called Self-Supervised Visual On-Policy Distillation (S^2VOPD) that generates learning signals from asymmetric augmented views of images, allowing for effective on-policy learning without privileged information. Practitioners might care about this paper because it presents a simple yet effective way to improve performance on various perception benchmarks.
This paper introduces FlowBalance, a method that helps a reasoning model improve itself by learning from its own experiences, while avoiding overconfidence and focusing on the best solutions. Practitioners might care about this approach because it can lead to more accurate and diverse model performance.
This paper addresses a problem in self-distillation, where a student model learns to mimic the behavior of a privileged teacher, but performs poorly at inference due to a "privilege illusion". The authors propose a new method, Dual-Anchored Policy Distillation, to resolve this issue and improve performance.