Title: I built a reward analysis tool for AI alignment — here's why reward hacking is harder to detect than you think

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.