SaveRouter Uses Sparse Supervision to Reduce LLM Routing Costs

SaveRouter is a routing framework that reduces the high upfront cost of learning model routers by using sparse supervision. It selectively acquires informative model feedback rather than executing all candidate models on historical queries, preventing economic over-provisioning during the training phase.