SBCO: Self-Supervised, Verifier-Grounded Harness Optimization For Planning Agents
A resource-efficient framework for self-improving planning agents without recursive code modification.
Self-improving agents seek to reduce the human engineering effort behind AI systems by enabling them to evolve and self-improve their performance over time. Recently, methods like the Darwin G\"odel Machine and the Huxley G\"odel Machine have been proposed which enable open-ended...
ArXiv CS.AI
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SBCO demonstrates that agents can achieve high-level self-improvement for complex planning tasks without the heavy compute requirements of recursive self-referential code modification. By grounding optimization in a decomposed bank of verifiers, this approach creates a more efficient pathway for training autonomous systems through experience-driven feedback.
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