RewardGuard is a Python library designed to monitor and address issues of reward hacking, misaligned incentives, and training stagnation in reinforcement learning (RL) workflows. Its primary function is to detect when RL agents exploit reward functions, providing safeguards to ensure that models behave as intended during training and production runs. The tool is aimed at users developing or maintaining RL systems who need to maintain alignment and trust in their AI models.
The platform offers several features to support RL training oversight. It enables real-time analysis of reward distributions, instantly flagging anomalies such as reward hacking patterns and misalignment. Users can visualize reward trends, helping to identify stagnation before it leads to wasted computational resources. RewardGuard also provides alignment reports in PDF format for documentation and auditing. For those using the Premium version, the tool can automatically adjust reward weights mid-training, requiring no manual intervention, and offers dynamic reward rebalancing and continuous monitoring. The system can export audit logs in CSV format, and advanced anomaly detection is available with premium credits.
RewardGuard integrates directly into Python-based RL training scripts and is compatible with any RL framework. Installation is accomplished via pip, and the tool can be configured with expected reward distributions and tolerances. It operates by analyzing raw training logs, monitoring reward signals step-by-step, and providing actionable recommendations or automated fixes when issues are detected. The tool does not require infrastructure changes and is designed for quick integration into existing workflows.
Pricing is structured with a free tier suitable for research, experimentation, and side projects, which includes basic reward signal analysis, trend detection, visualization, alerts, and recommendations for up to 100,000 training steps. Premium access is available on a pay-as-you-go basis, with analysis credits that never expire and additional features such as automatic parameter adjustment, priority support, and advanced detection capabilities. There are no recurring charges for premium credits, which are sold in bundles. RewardGuard positions itself as a solution for ensuring AI safety and transparency in reinforcement learning environments.
RewardGuard sits in PulseGate's LLM eval & observability category. It focuses on detecting and preventing reward hacking and misalignment in reinforcement learning agent training. RewardGuard is a B2B product aimed at machine learning engineers. A free plan is available. It runs on the command line and API.
RewardGuard first shipped in 2026. The project is developed in the open on GitHub with 18 commits in the last 90 days. Among its 5 catalogued features are reward monitoring, RL integration, and misalignment detection. It exposes integrations via a public API.
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