Retrospective Harness Optimization: Improving LLM Agents via Self-Preference over Trajectory Rollouts 事件

Retrospective Harness Optimization: Improving LLM Agents via Self-Preference over Trajectory Rollouts arXiv:2606.05922v1 Announce Type: cross Abstract: AI agents rely on a harness of skills, tools, and workflows to solve complex problems. Continually improving this harness is essential for adapting to new tasks. However, existing optimization methods typically require ground-truth validation sets, yet such labeled data is difficult to acquire in practical deployment settings. To address this pr