调优AI智能体：质量更高，成本更低
调优AI智能体通常只盯着回答质量，但真正部署时你还要付账单。这篇把两件事一起优化，却撞上一个坑：如果中途就按最终偏好淘汰候选，那些暂时不达标、但后面能派上大用场的方案会被提前扔掉。研究者把「中间候选的晋级」和「最终部署的选择」拆开：每轮只留下在质量或成本上确实有进步的方案，最后再按你的偏好挑。结果在两项测试里，质量分别从70.86%提到81.35%、56.22%提到62.89%，同时每个任务的成本还降了。它不是你明天能用上的工具，但提示了一个方向：别让最终目标过早地扼杀中间可能性。
📄 原文摘要(英文)
Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality. Extending them to joint quality--cost optimization introduces a challenge: applying the deployment preference during intermediate updates can prematurely reject candidates that are valuable for later improvements. We introduce ParetoSaddler, which addresses this mismatch by separating intermediate candidate promotion from final deployment selection. At each iteration, an optimizer updates a parent harness to produce a candidate, and then compares the two over repeated executions and uses Bayesian bootstrap Pareto support to promote candidates showing credible progress in either quality or cost. Promoted candidates receive broader development evaluation and remain available as parents for later optimization steps. We evaluate ParetoSaddler on ClawMark and GAIA2 using domain-disjoint splits. On ClawMark, ParetoSaddler improves quality from 70.86\% to 81.35\% while reducing cost from \1.17 to 1.01 per task; on GAIA2, it improves quality from 56.22\% to 62.89\% while reducing cost from \0.78 to 0.45 per task. The Pareto-selected harnesses also achieve higher quality at lower cost than quality-only harness optimization. Analysis shows that Pareto-aware optimization retains intermediate candidates that would be discarded by deployment preference but can support later improvements, while Bayesian bootstrap screening filters out candidates whose gains are weakly supported across runs.