微软论文提出 ActiveSaddler 自动优化智能体 harness
微软发表关于智能体 harness 自动优化的新论文，提出 ActiveSaddler 方法，通过追踪失败模式、优先处理最值得修复的失败或尝试未见过的任务来选择训练任务，而非使用固定任务列表。
New Microsoft paper on Automated harness optimization for agents.
Most harness auto-tuners focus on how to patch prompts and tools, but which tasks produce the feedback also changes how good the final harness gets.
But you will get stronger AI agents when you pick training tasks based on which failures are still unfixed, so stop feeding them a fixed task list.
ActiveSaddler tracks failure patterns and works on the one most worth fixing, or tries unseen tasks to find new ones.
On the same optimizer, ActiveSaddler raised test pass rates by 4.4 points on GAIA2 and 7.5 points on Terminal-Bench 2.0. Reaching 58.5% GAIA2 dev accuracy cost $298, versus $1,360 with a fixed order.
If you auto-tune an agent, aim your run budget at the failures that are still open.
来源：rohanpaul_ai · x.com