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This paper introduces HB-SJD, a batched speculative Jacobi decoding method for visual on-policy distillation that accelerates rollout generation by processing multiple tokens in parallel, reducing training time while preserving generation quality.
The paper introduces S^2VOPD, a self-supervised method that improves vision-language models by distilling from original images into strongly augmented student views, achieving performance surpassing GPT-5.4 on benchmarks.