ReHarness: Learning Reusable Harness Evolution Across Tasks

Large Language Model (LLM) agents increasingly rely on programmable harnesses to coordinate reasoning, tool use, and execution. Existing harness evolution methods typically optimize a task-specific harness from scratch for each new task, requiring substantial task-specific data and evolution steps. In this work, we identify that harness evolution exhibits reusable structure across heterogeneous tasks: independently evolved harnesses repeatedly discover similar improvement strategies, although the resulting harnesses remain task-specific. Building on this insight, we propose ReHarness, a reusable harness evolution framework that learns how to evolve across tasks. ReHarness learns a transferable evolution strategy from multiple offline-given tasks and applies it to rapidly adapt task-specific worker harnesses to new tasks using only a few examples. Experiments across four LLMs and six benchmarks demonstrate that ReHarness consistently improves agent performance on four new tasks not used during the offline phase, while outperforming the task-specific evolution method Self-Harness on two tasks from the offline suite under the same data budget. Code and models will be made publicly available.