A debugger for RL reward functions that detects reward hacking during training [P]

While experimenting with GRPO training, I kept running this shit that when reward increases, it becomes difficult to tell whether the policy is genuinely improving or simply exploiting the reward function. So I built a small library called rewardspy that wraps an existing reward function and continuously monitors indicators that often precede reward hacking.

It currently tracks things like rolling reward statistics, reward variance collapse, reward component imbalance, response length drift, reward slope changes, GRPO group collapse, anol.

This is my first major RL project so I would absolutely love some technical advice

Check it out here: https://github.com/AvAdiii/rewardspy