RLHF Learning
Continuous improvement from every merged fix, reviewer comment, and security approval - customized to your organization
Learning Signals
Positive Rewards
- Patches merged and remain stable
- Minimal reviewer edits required
- Accepted coding patterns
- Successful exploit elimination
Negative Feedback
- Patches rejected or heavily edited
- Rollbacks after deployment
- Missed incidents (false negatives)
- Down-scored findings
Adaptive Policies
Learning signals update scanner priorities, agent aggressiveness, finding rankings, and patch patterns. The system converges toward what reliably raises your organization's security floor with minimal noise.
Commit Security Timeline
W1
W7
W13
W19
W25
1
2
3
4
5
6
7
Less secure
More secure
Observable Improvements
Patch Acceptance
67%→94%
False Positives
23%→< 1%
Time to Fix
4.2 days→2.3 hours
Lines Changed
142→23