Show HN: RewardGuard – detect reward hacking in RL training loops (github.com)

RewardGuard, an AI alignment and safety tooling company, has launched a new GitHub repository aimed at detecting reward hacking in reinforcement learning (RL) models. This innovative tool analyzes RL training logs to ensure reward functions are properly aligned with intended goals, identifying unintended exploitation of rewards and providing actionable insights for remediation. Key features include reward distribution analysis, imbalance detection, and training diagnostics, which help developers understand reward dynamics and catch issues early in the training process. The significance of RewardGuard lies in its potential to enhance the safety and alignment of AI systems by preventing reward hacking, where agents may exploit flaws in reward structures rather than pursue intended objectives. The tool offers both a free version and a premium option that includes automatic reward rebalancing and live monitoring during training, making it a valuable asset for researchers and practitioners in the AI/ML community. This development addresses a critical aspect of AI safety, ensuring that models learn desired behaviors rather than merely maximizing scores through unintended shortcuts.