When you train an AI with reinforcement learning, the reward function is supposed to guide it toward the behavior you want. But what happens when the model finds ways to maximize reward without actually doing what you intended? That's reward hacking — and it's one of the core problems in AI alignment. I built RewardGuard to help detect and analyze reward imbalances in RL systems. It's a Python package available on PyPI with a free tier (rewardguard) and a premium tier (rewardguard_premium) for deeper analysis. Here's what it does: Analyzes reward signal distribution across training episodes Flags anomalies that suggest reward hacking behavior Generates balance reports to help you understand where your reward function might be failing If you're interested, check it out at rewardguard.dev or install it directly: pythonpip install rewardguard For usage details and examples, the docs are at rewardguard.dev/docs. I'm still early in the journey of getting this out to people who actually need it.…