Why Your RL Agent Is Cheating (And How to Catch It)
Every reinforcement learning agent has one goal: maximize its reward. The problem is that agents are extraordinarily creative at finding ways to score high that have nothing to do with what you actually wanted. We call this reward hacking — and it's more common than you think.
Clash Royale RL Championship — 3,000,000 Credits Prize Pool
Train a Clash Royale RL agent with RewardGuard and compete for 3M credits and an official certificate. Free entry. Competition runs May 30 – June 30, 2026.
Read more →Logging Reward Changes Mid-Training: Free & Premium Guide
A complete walkthrough of both tiers — rolling-window balance checks with the free Monitor, and per-step correction logs, CSV export, and WandB/TensorBoard callbacks with AutoMonitor.
Read more →The Survival vs. Food Trade-off: A Case Study in Reward Imbalance
Using a simple snake environment, we show how a single miscalibrated reward coefficient can cause an agent to converge on the wrong strategy entirely — and how to detect it before it derails your model.
Read more →RLHF Pitfalls: When Human Feedback Creates Bad Incentives
Reinforcement Learning from Human Feedback is powerful — but it introduces its own alignment risks. We explore how models learn to game human raters and what monitoring can catch it early.
Read more →Reward Balance Scores: How RewardGuard Quantifies Misalignment
Behind the scenes of RewardGuard's detection engine — how we compute reward ratios, establish dynamic thresholds, and assign confidence scores to detected anomalies.
Read more →Getting Started with RewardGuard: Your First Training Run Audit
A step-by-step walkthrough for integrating RewardGuard into an existing PyTorch training loop. From installation to your first misalignment report in under 10 minutes.
Read more →Goodhart's Law and the RL Agent: Why Metrics Fail Under Optimization
"When a measure becomes a target, it ceases to be a good measure." We examine how Goodhart's Law manifests in modern RL training and what it means for reward function design.
Read more →Why We Open-Sourced the Detection Layer
We believe safety tooling should be accessible to everyone. Here's our thinking behind making RewardGuard's core detection engine MIT-licensed — and what stays in the premium tier.
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