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GitHub Repository
Plug-and-play reward monitoring for RL training loops. Catch reward hacking, component imbalance, and starvation before they tank your run. Drop in one .step() call — get balance reports, auto weight correction, alignment scores, and WandB/TensorBoard/SB3 integrations out of the box. → rewardguard.dev
5 starsPython
RewardGuard – detect reward hacking in RL training loops
by Giovan321·Apr 26, 2026·1 point·1 comment
AI Analysis
●●SolidNiche GemBig Brain
Catches reward hacking before it tanks your RL training run.
Strengths
- •Targets reward hacking specifically, a genuinely hard RL debugging problem
- •Integrates with existing RL stack: WandB, TensorBoard, Stable Baselines3
- •Clear output format with actionable weight adjustment recommendations
Weaknesses
- •Premium auto-adjustment features are private, can't evaluate full value
- •Very early stage: 2 stars, zero issues or pull requests on GitHub
Category
Target Audience
Reinforcement learning engineers and ML researchers
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Weights & Biases · TensorBoard · MLflow
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