RewardGuard catches misaligned incentives, training stagnation, and reward hacking in your RL — before it derails your model.
Integrate RewardGuard into your existing workflow in minutes — no infrastructure changes needed.
Install RewardGuard via pip and import it directly into your Python training script. Works with any RL framework.
Deep analysis of reward signals, distributions, and temporal patterns.
Instantly flags reward hacking, misalignment, and stagnation patterns.
Get recommendations or let Premium auto-adjust your reward parameters.
Monitor reward distributions live as your model trains. Catch anomalies the instant they appear — not after 100,000 wasted compute steps.
Active protection against reward hacking during production training runs.
Detection accuracy across 2M+ analyzed episodes
Visualize reward trends and spot training stagnation before it costs you compute budget.
Export detailed PDF reports for documentation and auditing purposes.
Automatically tunes reward weights mid-run. Zero manual intervention required.
Start free. Detect and prevent reward hacking in your RL.
For research, experimentation, and side projects.
Buy analysis credits once — no subscription, no recurring charge.
Everything you need to integrate RewardGuard into your existing RL training workflow.
Add the free or premium package to your environment.
Initialize with your environment settings.
Run analysis on your existing training data.
Enable automatic reward parameter adjustment.
Misaligned AI is not a future risk — it's happening right now in every RL training run. RewardGuard is our answer.
Every feature we build starts with one question: does this make AI systems safer and more predictable?
The reward function is the soul of your AI. We ensure it incentivizes the right behaviors, not just high scores through shortcuts.
Understand exactly what your AI is learning at every step. Clear insights, no black boxes.
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