You fine-tune a robot foundation model on a hard task. It gets 25%. Now what? Introducing Q-Planning, a learning-based harness that lets large black-box robot policies recursively self-improve. On a hard fine-grained task: 25% → 80% in 100 robot attempts (~30 mins), no extra human data. q-planning.github.io/ 1/n 🧵