TL;DR of the paper, generated automatically.

A training method that learns from alternate agent setups when the base setup fails.

• How: failed runs get swapped for runs from sibling harnesses.
• Results: substantially beats policy-only training on AppWorld, SWE-Gym, DBBench.
• Router: picks the top-k variants using behavior profiles and voting across LLMs.