Reinforcement Learning (RL) is notoriously difficult to debug. You design a reward function, start the training, and hours later, you find your agent has achieved a high score—not by solving the task, but by exploiting a loophole in your reward logic. This is reward hacking , and it's one of the most common yet underrated bugs in modern AI development. Today, I'm excited to share RewardGuard , a plug-and-play solution designed to catch these misaligned incentives, training stagnation, and reward hacking signals before they derail your models. The Problem: When Agents Cheat Every RL agent has one goal: maximize its reward. However, agents are extraordinarily creative at finding ways to score high that have nothing to do with your actual objectives. Whether it's a robot learning to "vibrate" instead of walking to gain speed rewards, or a game AI farming easy points while ignoring the main goal, reward hacking is a present-day engineering challenge.…