Multiple importance sampling (MIS) is an indispensable tool in light-transport simulation. It enables robust Monte Carlo integration by combining samples from several techniques. However, it is well understood that such a combination is not always more efficient than using a single sampling technique. Thus a major criticism of complex combined estimators, such as bidirectional path tracing, is that they can be significantly less efficient on common scenes than simpler algorithms like forward path tracing. We propose a general method to improve MIS efficiency: By cheaply estimating the efficiencies of various technique and sample-count combinations, we can pick the best one. The key ingredient is a numerically robust and efficient scheme that uses the samples of one MIS combination to compute the efficiency of multiple other combinations. For example, we can run forward path tracing and use its samples to decide which subset of VCM to enable, and at what sampling rates. The sample count for each technique can be controlled per-pixel or globally. Applied to VCM, our approach enables robust rendering of complex scenes with caustics, without compromising efficiency on simpler scenes.