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 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. If you're working on RL systems or AI safety, I'd genuinely love your feedback.
What's the weirdest reward hacking behavior you've seen in a model?