AI systems can improve other AI systems' performance by learning to build better execution environments—a form of test-time optimization that's reusable across tasks without modifying model weights.
This paper studies how an AI system (Builder) can learn to design better execution environments for another AI system (Target) without changing either model's weights. The Builder learns reusable principles called Meta-Skills from feedback on development tasks, then applies these to construct better environments for new tasks. Results show significant performance improvements across benchmarks.