Safety & Policy Red-Team Gym
Adversarial RLVR environment for policy robustness, reward hacking resistance, and controlled regression testing.
Case Study: Safety & Policy Red-Team Gym
Program Type: High-selectivity engagement
Domain: Frontier policy and model risk
Process
Founders with prior RLVR environment experience from Anthropic and Google DeepMind programs led this build. We implemented adversarial task families that intentionally induced shortcut behavior, then used verification checkpoints to detect reward hacking early. The harness supported controlled regressions so teams could measure if policy updates improved robustness or simply shifted failure modes.
Outcome
The gym gave stakeholders a repeatable method to compare model behavior across policy revisions. Teams identified brittle reward shaping earlier and reduced high-severity policy violations in evaluation runs. This became a core part of pre-release acceptance testing for safety-critical scenarios.