Real-world experimentation often involves making complex tradeoffs between many outcomes or targeting noisy or long-term objectives that are difficult to measure. This problem can be particularly challenging in the context of continuous action spaces where an infinite number of tradeoffs can be achieved. I will discuss these problems in the context of real-world problems faced by experimenters at Meta. I will discuss solutions to the many objective problem via learning from human feedback. I will conclude with strategies for targeting long-term outcomes and open questions.