reward-free

Rank-Then-Act (RTA) is a framework for learning control policies from expert video demonstrations without environment rewards, using a Vision-Language Model as a progress-based ordinal scorer with correlation-based rewards. It achieves stable cross-task transfer and outperforms prior methods on discrete and continuous control benchmarks.