Differentiable Efficient Operator Search
Efficient multimodal foundation models often rely on manually designed token-reduction operators, such as pruning, merging, pooling, and adaptive reweighting. Although these operators appear different, we show that they can be interpreted as distinct regimes of a shared operator space. Based on this view, we introduce Efficient Operator Search, a differentiable framework that jointly searches where to reduce tokens, how many tokens to retain, and how reduced token information should be processed. The proposed search space parameterizes layer activation, retention budget, and operator behavior, while the search policy optimizes task performance under one-sided budget and cost constraints. ...
| Authors | Xiaohuan Pei, Jiyuan Zhang, Yuanfan Guo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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