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RubricForge induces evaluation rubrics from labeled trajectories to reduce over-crediting in language model agent evaluation, enhancing faithfulness without environment access.
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.