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.…