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Researchers propose a set-level reinforcement learning objective that trains language models to generate diverse, high-quality, document-grounded scientific ideas, with an 8B model outperforming Claude Opus 4.8 and GPT-6 Astra on creative utility.
This paper presents the first systematic study of faithfulness in document-grounded podcast generation, introducing a turn-level LLM-as-a-judge evaluation framework and a model-agnostic catch-n-repair method that improves faithfulness across domains.