SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models
Reasoning vision-language models (VLMs) process images and videos as long sequences of visual tokens, making inference expensive. Training-free token pruning reduces this cost, but aggressive compression can sharply degrade performance, often attributed to irreversible loss of task-relevant visual information. We show...
Ahmadreza Jeddi, En-Ming Zhang, Jasper Gerigk et al.
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