Safety & Policy Red-Team Gym

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.