ActiveSaddler: Automated Curriculum Learning for Agent Harness Optimization

Excerpt

arXiv:2610.00906v1 Announce Type: cross Abstract: Automated harness optimization can substantially improve LLM agents by iteratively updating their prompts, tool interfaces, and control logic from execution feedback. However, existing methods primarily optimize how the harness is updated while largely fixing which training scenarios generate the feedback that drives those updates. As the harness evolves, the scenarios most useful for further optimization can change, suggesting that the training