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 problem, we introduce Retrospective Harness Optimization (RHO), a self-supervised method that optimizes the agent harness using only past trajectories. Specifically, RHO selects a diverse coreset of challe
The rapid development of AI agents has created an urgent need for self-improvement mechanisms, particularly as their complexity and autonomy increase.
This research addresses a critical limitation in deploying AI agents by enabling their improvement without constant human intervention or expensive labeled data, paving the way for more robust and adaptable systems.
AI agent optimization can now be achieved through self-preference over past trajectories, shifting away from a sole reliance on ground-truth validation sets.
- · AI agent developers
- · Companies deploying AI agents
- · Generative AI platforms
- · Automation software providers
- · Manual AI system calibration services
- · Organizations slow to adopt autonomous AI solutions
AI agents become more capable and adaptable in diverse real-world environments.
Increased efficiency and autonomy of AI systems could lead to acceleration in various white-collar workflows and industry sectors.
The development of truly autonomous and self-improving AI agents could eventually lead to profound changes in the nature of work and economic structures.
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