SBCO: Self-Supervised Verifier-Grounded Harness Optimization for Planning Agents
A new arXiv preprint (2608.10157) introduces SBCO, a method for optimizing planning agents without self-referential self-improvement. The approach is designed for tasks where the competence required for the task does not align with the competence required for self-modification, making self-referential methods like Darwin and Huxley Gődel Machines inapplicable. SBCO uses a self-supervised, verifier-grounded harness to optimize the agent's planning capabilities, avoiding the computational expense of population-based or explicit meta-agent self-modification. The method is presented as a way to enable self-improvement in domains beyond coding, where such alignment is lacking. The paper is authored by researchers and posted on arXiv, indicating ongoing work in AI self-improvement.
Key facts
- arXiv:2608.10157
- SBCO stands for Self-Supervised, Verifier-Grounded Harness Optimization
- Targets planning agents
- Addresses tasks where self-referential self-improvement is not feasible
- Contrasts with Darwin and Huxley Gődel Machines
- Avoids population-based or explicit meta-agent self-modification
- Uses self-supervised learning and verifier grounding
- Published on arXiv
Entities
Institutions
- arXiv