AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes Visual Contrastive Self-Distillation, namely VCSD, which converts image-content removal into an on-policy self-distillation signal, and consistently outperforms matched OPSD across Qwen3-VL and Qwen3.5 models.
Self-Supervised Visual On-Policy Distillation (S$^2$VOPD), a simple yet effective method that constructs on-policy learning signals from asymmetric augmented views, systematically explores a broad design space of visual augmentations and uncover that asymmetry matters.
Clue-OPSD, a clue-privileged on-policy self-distillation framework for long-video understanding that uses clue intervals as privileged supervision without relying on ground-truth answer labels, while requiring no clue annotations or additional modules at inference time is introduced.
U-OPSD first samples multiple rollouts and constructs a pseudo solution by majority vote under a self-consistency threshold, and conditions the model's distribution on the pseudo-solution and distills itself on the disagreeing completions, allowing the model to correct itself precisely where it is confidently wrong.
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