Making Search Agents Faster and Smarter

This research examines optimization strategies for search agents across the retrieval tool and the planning mechanism, finding that the reranker matters most for tool configuration. Training the planner through on-policy distillation combined with a novel efficiency reward (CLP) enables a faster-configured system to match stronger baselines at half the latency while reaching 60.7% accuracy on their primary benchmark.