Differentiable Efficient Operator Search

Efficient vision-language models have largely relied on human-designed reduction operators, such as pruning, merging, pooling, and adaptive reweighting, which could happen at any time and anywhere. We show that these seemingly different methods can be unified as different operating regimes of a single shared operator space. Based on this observation, we introduce **Efficient Operator Search**, a unified framework in which continuous parameters control whether token information is removed, sharply merged, uniformly pooled, or softly redistributed. Instead of hand-crafting operator compositions, we define a ***efficient search space***: a differentiable parameterization of layer activation, retention budget, and operator regime, and a ***efficient search policy***: minimizing the expectation of the task loss under one-sided budget and cost constraints. The pipeline leads to a broader shift from an ***efficient design problem*** to an ***operator search problem***, suggesting a new paradigm for future efficient modeling. Interestingly, we find that most prior mainstream baselines can be treated as special cases of this shared operator space, and further reveal consistent operator patterns across different benchmarks. Even as an early exploration of this new paradigm, our proposed method achieves competitive results on various benchmarks. We believe that it provides a unified view of prior methods, a principled lens for understanding current differences, and a general foundation for future efficient modeling.