By separating strategy exploration from implementation and reusing prompt prefixes across evolution steps, you can achieve better optimization results while spending 50-100x less on LLM API calls.
FrugalEvo optimizes LLM-guided program evolution by pairing a powerful LLM that explores strategies with a cheaper LLM that implements them, while using cache-efficient prompting to reduce costs. It introduces Budget-Aware AUC to measure solution quality per dollar spent, achieving state-of-the-art results on optimization tasks at a fraction of the cost of competing methods.