Imagine a world where AI could seamlessly switch between different skills, mastering everything from crafting creative stories to solving complex math problems. Researchers are tackling this very challenge, exploring how to make AI more adaptable and efficient by dynamically selecting the most appropriate reward model for each task. Traditionally, training AI models involved using a single, fixed reward model to guide their learning. This approach often falls short when dealing with diverse tasks, as a reward model optimized for one area might hinder performance in another. For instance, a reward model focused on factual accuracy might stifle creativity in a storytelling task, while a creativity-focused model could lead to inaccuracies in mathematical reasoning. This new research introduces LASER (Learning to Adaptively Select Rewards), a groundbreaking technique that treats reward model selection as a multi-armed bandit problem. Just like a gambler choosing the best slot machine, LASER dynamically selects the most promising reward model for each task, based on contextual information and past performance. This adaptive approach allows AI models to learn more effectively from multiple reward models, without the computational burden of using all of them simultaneously. The results are impressive. LASER consistently outperforms traditional methods across a variety of tasks, from reasoning and instruction-following to long-context understanding. In essence, LASER acts like a personalized tutor for AI, choosing the right teaching method for each subject. This dynamic approach not only improves performance but also enhances efficiency, as the AI model focuses on the most relevant reward signals. This breakthrough has far-reaching implications for the future of AI, paving the way for more versatile and capable models that can seamlessly adapt to diverse challenges. While promising, the research also highlights the importance of high-quality reward models. LASER effectively mitigates the impact of noisy or irrelevant reward models, but the success of this approach ultimately depends on the availability of relevant reward models for each task. As AI continues to evolve, the focus is shifting from specialized models to adaptable, multi-skilled systems. LASER represents a significant step in this direction, enabling AI to unlock its full potential by learning from the best reward model for every task, ultimately leading to more robust and capable AI systems.