The paper presents UpliftMem, a method for learning memory retrieval for large language model (LLM) agents. UpliftMem learns which memory sets improve execution without relying on costly outcome feedback, instead using set-level execution uplift relative to the same executor without memory. This approach is evaluated on three benchmarks (ALFWorld, WebShop, and BigCodeBench) and outperforms other baselines.