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This paper introduces transition-aware best-of-N sampling, a training-free method for generating longitudinal chest X-ray reports by encoding changes between prior and current examinations using set-to-set distance metrics.
This paper proposes set-distance rewards for reinforcement learning in chest X-ray report generation, using embedding-based set-to-set distances between generated and reference reports. Post-training with these rewards via GRPO consistently outperforms supervised fine-tuning and exact-match rewards, and enables efficient test-time scaling.