This academic paper introduces Paprika, a novel fine-tuning method designed to enhance the exploratory and decision-making capabilities of language models. Unlike traditional training, Paprika focuses on teaching models to adapt to new tasks by learning from synthetic interaction data, rather than through continuous gradient updates. The research emphasizes the importance of strategic information gathering for intelligent systems and proposes a curriculum learning strategy to improve the efficiency of sampling useful data. The authors suggest this approach offers a promising direction for AI systems capable of autonomously solving novel sequential decision-making problems that require interaction with the real world.