Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models 事件

Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models arXiv:2605.28051v1 Announce Type: new Abstract: Visual token pruning reduces the computational cost of Vision-Language Models (VLMs) by removing redundant visual tokens. Existing methods typically rely on Gumbel-Softmax to approximate discrete selection during training. However, the optimization is driven by surrogate gradients rather than the true selection process, leading to unreliable learning of token