Imagine an AI that not only predicts but learns and improves its predictions over time. That's the power of reinforcement learning applied to prediction models, a technique explored by researchers at Ringier Axel Springer Polska, one of the largest media companies in Poland. Their news recommendation system, Aureus, handles a massive volume of user traffic – over a thousand requests per second! To tackle this scale, they needed a system that could personalize recommendations while keeping up with the constant influx of new articles. Their secret weapon? Combining traditional prediction models with reinforcement learning techniques like multi-armed bandits. Think of it like training a dog: the AI gets rewarded (in this case, with engagement metrics) for good predictions and learns to avoid less successful ones. This approach helps overcome the 'cold start problem', where new users haven't interacted with the system enough to generate personalized recommendations. Instead of relying only on past user behavior, the system explores and exploits trending content, ensuring users discover fresh articles. But it's not just about popularity. Aureus also incorporates deep learning models based on Large Language Models (LLMs) to capture individual user preferences. The researchers experimented with different ways of combining these models—like blending ingredients in a recipe—and found that a 'weighted average' approach performed best. This means each model's prediction gets a weighted vote, creating a balanced recommendation that caters to both individual tastes and trending topics. Their findings highlight the significant boost in online engagement metrics achieved by combining reinforcement learning with prediction models. While deep learning added some latency, it remained within acceptable limits and contributed significantly to the system's overall effectiveness. This research opens up exciting possibilities for the future of AI, showing how reinforcement learning can supercharge prediction models across various fields, from personalized recommendations to dynamic pricing and even medical diagnoses.