Turbo Harness: Instance-Adaptive Harness Optimization

Designing effective harnesses with minimal human intervention is an important challenge for building modern agents. Existing automated harness optimization methods typically produce a single global harness that is applied uniformly across future problem instances. However, a harness that works well on average may not be optimal for every instance. We introduce turbo harness, a framework that can adapt a globally optimized harness to each individual instance by reusing information generated during the original optimization process. Specifically, turbo harness recycles artifacts, including execution trajectories and reflections, produced during a completed global harness optimization run. We train a harness editor, an RL-fine-tuned open-source model, to leverage these artifacts to generate instance-specific patches to the globally optimized harness. At inference time, the proposer uses the instance and prior optimization experience to construct a tailored harness for the execution model. We validate our method through comprehensive numerical experiments.